{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/np/Downloads/PetrovskyJamesMoseleyBoynePAR.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 9 Mar 2017, 17:06:51
{txt}
{com}. 
. * Do-file for Petrovsky/James/Moseley/Boyne PAR article: 
. * "What Explains Agency Heads' Length of Tenure? Testing 
. * Managerial Background, Performance, and Political 
. * Environment Effects"
. 
. * Nick Petrovsky/Oliver James
. * Contact: nicolai.petrovsky@uky.edu
. 
. * Stata version 13.1
. * code last modified on May 18, 2016
. * annotations edited for clarity on March 9, 2017
. 
. * Data set required:
. * PetrovskyJamesMoseleyBoynePAR_full.dta
. 
. * Note: the dataset PetrovskyJamesMoseleyBoynePAR_full.dta that 
. * we posted on the Harvard Dataverse excludes the following two 
. * variables: 
. * 1) ceodob (the birthday of the agency chief executive)
. * 2) ceoname (the name of the agency chief executive)
. 
. * Please copy this do-file and the data set into a
. * directory on your hard drive.
. * Then, please use Stata's command
. * cd
. * to change to that directory.
. * Now you can run this do-file.
. 
. clear
{txt}
{com}. clear matrix
{txt}
{com}. version 13.1
{txt}
{com}. set more off
{txt}
{com}. set scheme s1mono
{txt}
{com}. 
. * Open data and save as temporary working file
. use PetrovskyJamesMoseleyBoynePAR_full.dta
{txt}(output_cont)

{com}. save workfile, replace
{txt}(note: file workfile.dta not found)
file workfile.dta saved

{com}. 
. * Prepare variables: 
. 
. * Adjust centralspend for inflation
. 
. * We use the following variable for turning the amount 
. * of central government spending into constant GBP:
. * "Gross national expenditure deflator" drawn from the 
. * World Bank data online
. * (accessed on April 30, 2013)
. * (The base year for the United Kingdom is 2005; so, 
. * after deflating, the variables are in 2005 GBP.)
. replace centralspend = centralspend/0.5948 if /*
> */ financialyear == 1989
{txt}(3 real changes made)

{com}. replace centralspend = centralspend/0.6377 if /*
> */ financialyear == 1990
{txt}(12 real changes made)

{com}. replace centralspend = centralspend/0.6828 if /*
> */ financialyear == 1991
{txt}(33 real changes made)

{com}. replace centralspend = centralspend/0.7222 if /*
> */ financialyear == 1992
{txt}(54 real changes made)

{com}. replace centralspend = centralspend/0.7492 if /*
> */ financialyear == 1993
{txt}(69 real changes made)

{com}. replace centralspend = centralspend/0.7699 if /*
> */ financialyear == 1994
{txt}(83 real changes made)

{com}. replace centralspend = centralspend/0.7845 if /*
> */ financialyear == 1995
{txt}(89 real changes made)

{com}. replace centralspend = centralspend/0.8097 if /*
> */ financialyear == 1996
{txt}(96 real changes made)

{com}. replace centralspend = centralspend/0.8366 if /*
> */ financialyear == 1997
{txt}(111 real changes made)

{com}. replace centralspend = centralspend/0.8581 if /*
> */ financialyear == 1998
{txt}(105 real changes made)

{com}. replace centralspend = centralspend/0.8749 if /*
> */ financialyear == 1999
{txt}(107 real changes made)

{com}. replace centralspend = centralspend/0.8911 if /*
> */ financialyear == 2000
{txt}(104 real changes made)

{com}. replace centralspend = centralspend/0.8999 if /*
> */ financialyear == 2001
{txt}(102 real changes made)

{com}. replace centralspend = centralspend/0.9127 if /*
> */ financialyear == 2002
{txt}(97 real changes made)

{com}. replace centralspend = centralspend/0.9299 if /*
> */ financialyear == 2003
{txt}(94 real changes made)

{com}. replace centralspend = centralspend/0.9491 if /*
> */ financialyear == 2004
{txt}(95 real changes made)

{com}. replace centralspend = centralspend/0.9722 if /*
> */ financialyear == 2005
{txt}(92 real changes made)

{com}. * FY 2005/06 spending mostly takes place in the 
. * base year of 2005 and therefore does not get adjusted.  
. replace centralspend = centralspend/1.0306 if /*
> */ financialyear == 2007
{txt}(84 real changes made)

{com}. replace centralspend = centralspend/1.0528 if /*
> */ financialyear == 2008
{txt}(75 real changes made)

{com}. replace centralspend = centralspend/1.0900 if /*
> */ financialyear == 2009
{txt}(64 real changes made)

{com}. replace centralspend = centralspend/1.1059 if /*
> */ financialyear == 2010
{txt}(68 real changes made)

{com}. replace centralspend = centralspend/1.1388 if /*
> */ financialyear == 2011
{txt}(60 real changes made)

{com}. replace centralspend = centralspend/1.1747 if /*
> */ financialyear == 2012
{txt}(57 real changes made)

{com}. 
. * Generate the university degree dummies
. gen advdegree = 0 if ceoquals != .
{txt}(69 missing values generated)

{com}. replace advdegree = 1 if ceoquals == 2 | ceoquals == 3
{txt}(657 real changes made)

{com}. tab advdegree ceoquals

           {txt}{c |}                  ceoquals
 advdegree {c |} No degree     Degree    Masters        PhD {c |}     Total
{hline 11}{c +}{hline 44}{c +}{hline 10}
         0 {c |}{res}       423        695          0          0 {txt}{c |}{res}     1,118 
{txt}         1 {c |}{res}         0          0        402        255 {txt}{c |}{res}       657 
{txt}{hline 11}{c +}{hline 44}{c +}{hline 10}
     Total {c |}{res}       423        695        402        255 {txt}{c |}{res}     1,775 

{txt}
{com}. gen badegree = 0 if ceoquals != .
{txt}(69 missing values generated)

{com}. replace badegree = 1 if ceoquals == 1 | ceoquals == 2 | ceoquals ==3
{txt}(1,352 real changes made)

{com}. tab badegree ceoquals

           {txt}{c |}                  ceoquals
  badegree {c |} No degree     Degree    Masters        PhD {c |}     Total
{hline 11}{c +}{hline 44}{c +}{hline 10}
         0 {c |}{res}       423          0          0          0 {txt}{c |}{res}       423 
{txt}         1 {c |}{res}         0        695        402        255 {txt}{c |}{res}     1,352 
{txt}{hline 11}{c +}{hline 44}{c +}{hline 10}
     Total {c |}{res}       423        695        402        255 {txt}{c |}{res}     1,775 

{txt}
{com}. 
. * Generate CEO age on April 1 of year t
. forval t = 1989(1)2012 {c -(}
{txt}  2{com}.    generate ceoage`t'=( mdy(4,1,`t') - ceodob ) / 365.25 if financialyear == `t'
{txt}  3{com}. {c )-}
{txt}(1,841 missing values generated)
(1,832 missing values generated)
(1,811 missing values generated)
(1,790 missing values generated)
(1,777 missing values generated)
(1,763 missing values generated)
(1,757 missing values generated)
(1,749 missing values generated)
(1,735 missing values generated)
(1,741 missing values generated)
(1,739 missing values generated)
(1,742 missing values generated)
(1,744 missing values generated)
(1,749 missing values generated)
(1,753 missing values generated)
(1,752 missing values generated)
(1,759 missing values generated)
(1,760 missing values generated)
(1,766 missing values generated)
(1,773 missing values generated)
(1,784 missing values generated)
(1,782 missing values generated)
(1,788 missing values generated)
(1,793 missing values generated)

{com}. gen ceoage = .
{txt}(1,844 missing values generated)

{com}. forval t = 1989(1)2012 {c -(}
{txt}  2{com}.    replace ceoage = ceoage`t' if financialyear == `t'
{txt}  3{com}. {c )-}
{txt}(3 real changes made)
(12 real changes made)
(33 real changes made)
(54 real changes made)
(67 real changes made)
(81 real changes made)
(87 real changes made)
(95 real changes made)
(109 real changes made)
(103 real changes made)
(105 real changes made)
(102 real changes made)
(100 real changes made)
(95 real changes made)
(91 real changes made)
(92 real changes made)
(85 real changes made)
(84 real changes made)
(78 real changes made)
(71 real changes made)
(60 real changes made)
(62 real changes made)
(56 real changes made)
(51 real changes made)

{com}. forval t = 1989(1)2012 {c -(}
{txt}  2{com}.    drop ceoage`t'
{txt}  3{com}. {c )-}
{txt}
{com}. histogram ceoage
{txt}(bin={res}32{txt}, start={res}34.609173{txt}, width={res}.99033201{txt})
{res}{txt}
{com}. 
. * Generate percentage of targets met including milestones
. gen targsmiles_set = ptargsreported+mile_set
{txt}(102 missing values generated)

{com}. replace mile_achieved = 0 if mile_achieved == .
{txt}(1,556 real changes made)

{com}. gen targsmiles_met = mile_achieved+ ptargsmet
{txt}(103 missing values generated)

{com}. gen targsmilespct_met = (targsmiles_met/targsmiles_set)*100
{txt}(104 missing values generated)

{com}. label variable targsmilespct_met "Percentage of targets met including milestones"
{txt}
{com}. replace targsmilespct_met = 100 if targsmilespct_met > 100 & targsmilespct_met ~= .
{txt}(0 real changes made)

{com}. sum targsmilespct_met

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
targsm~t_met {c |}{res}      1,740    77.51023    20.85973          0        100
{txt}
{com}. codebook targsmilespct_met

{txt}{hline}
{res}targsmilespct_met{right:Percentage of targets met including milestones}
{txt}{hline}

{col 19}type:  numeric ({res}float{txt})

{col 18}range:  [{res}0{txt},{res}100{txt}]{col 55}units:  {res}1.000e-07
{col 10}{txt}unique values:  {res}128{col 51}{txt}missing .:  {res}104{txt}/{res}1,844

{txt}{col 19}mean:{res}{col 26} 77.5102
{txt}{col 15}std. dev:{res}{col 26} 20.8597

{txt}{col 12}percentiles:{col 32}10%{col 42}25%{col 52}50%{col 62}75%{col 72}90%
{res}{col 27}      50{col 37} 63.6364{col 47} 81.8182{col 57}     100{col 67}     100
{txt}
{com}. sort agencyID financialyear
{txt}
{com}. gen L_targsmilespct_met = L.targsmilespct_met
{txt}(303 missing values generated)

{com}. 
. * Position of agency relative to all others that year (again, 1 sd above or below)
. * Obtain the means and sd's of targsmilespct_met for each financial year
. bysort financialyear: egen tarmil_FYmean = mean(targsmilespct_met)
{txt}(3 missing values generated)

{com}. bysort financialyear: egen tarmil_FYsd = sd(targsmilespct_met)
{txt}(15 missing values generated)

{com}. 
. * Define the tresholds
. gen lowtreshold_FY = tarmil_FYmean - tarmil_FYsd
{txt}(15 missing values generated)

{com}. gen hightreshold_FY = tarmil_FYmean + tarmil_FYsd
{txt}(15 missing values generated)

{com}. 
. * Generate low and high dummies
. gen low_tarmil_FY = 0 if targsmilespct_met ~= .
{txt}(104 missing values generated)

{com}. replace low_tarmil_FY = 1 if targsmilespct_met < lowtreshold_FY
{txt}(295 real changes made)

{com}. gen high_tarmil_FY = 0 if targsmilespct_met ~= .
{txt}(104 missing values generated)

{com}. replace high_tarmil_FY = 1 if targsmilespct_met > hightreshold_FY & targsmilespct_met ~= .
{txt}(318 real changes made)

{com}. sort agencyID financialyear
{txt}
{com}. gen L_low_tarmil_FY = L.low_tarmil_FY
{txt}(303 missing values generated)

{com}. gen L_high_tarmil_FY = L.high_tarmil_FY
{txt}(303 missing values generated)

{com}. 
. * Generate dummies for increases and decreases in target achievement relative 
. * to the previous year
. gen byte higher_score = 1 if targsmilespct_met > L_targsmilespct_met & /*
> */ targsmilespct_met ~= . & L_targsmilespct_met ~= .
{txt}(1,236 missing values generated)

{com}. replace higher_score = 0 if targsmilespct_met <= L_targsmilespct_met & /*
> */ targsmilespct_met ~= . & L_targsmilespct_met ~= .
{txt}(885 real changes made)

{com}. gen L_higher_score = L.higher_score
{txt}(535 missing values generated)

{com}. gen byte lower_score = 1 if targsmilespct_met < L_targsmilespct_met & /*
> */ targsmilespct_met ~= . & L_targsmilespct_met ~= .
{txt}(1,263 missing values generated)

{com}. replace lower_score = 0 if targsmilespct_met >= L_targsmilespct_met & /*
> */ targsmilespct_met ~= . & L_targsmilespct_met ~= .
{txt}(912 real changes made)

{com}. gen L_lower_score = L.lower_score
{txt}(535 missing values generated)

{com}. 
. * Generate media stories within-agency standard 
. * deviation indicator: 
. sort agencyID
{txt}
{com}. by agencyID: egen mean_media = mean(mediastories)
{txt}
{com}. by agencyID: egen SD_media = sd(mediastories)
{txt}(6 missing values generated)

{com}. gen media_Z_score = (mediastories - mean_media)/SD_media
{txt}(51 missing values generated)

{com}. sort agencyID financialyear
{txt}
{com}. gen L_media_Z_score = L.media_Z_score
{txt}(280 missing values generated)

{com}. 
. * Generate interaction of high/low target achievement dummies and 
. * media stories Z-score
. gen L_high_TA_X_media = L_high_tarmil_FY * media_Z_score
{txt}(336 missing values generated)

{com}. gen L_low_TA_X_media = L_low_tarmil_FY * media_Z_score
{txt}(336 missing values generated)

{com}. gen L_high_TA_X_L_media = L_high_tarmil_FY * L_media_Z_score
{txt}(336 missing values generated)

{com}. gen L_low_TA_X_L_media = L_low_tarmil_FY * L_media_Z_score
{txt}(336 missing values generated)

{com}. 
. * Generate agency function dummies
. gen internal = 1 if function == 1
{txt}(1,197 missing values generated)

{com}. replace internal = 0 if function ~= 1 & function ~= .
{txt}(1,197 real changes made)

{com}. gen external = 1 if function == 2
{txt}(1,126 missing values generated)

{com}. replace external = 0 if function ~= 2 & function ~= .
{txt}(1,126 real changes made)

{com}. gen regulatory = 1 if function == 3
{txt}(1,515 missing values generated)

{com}. replace regulatory = 0 if function ~= 3 & function ~= .
{txt}(1,515 real changes made)

{com}. gen research = 1 if function == 4
{txt}(1,694 missing values generated)

{com}. replace research = 0 if function ~= 4 & function ~= .
{txt}(1,694 real changes made)

{com}. save, replace
{txt}file workfile.dta saved

{com}. 
. * Generate political change dummies
. gen byte newparty2 = 0
{txt}
{com}. replace newparty2 = 1 if financialyear == 1998 | /*
> */ financialyear == 2011
{txt}(165 real changes made)

{com}. gen L_newparty2 = L.newparty2
{txt}(245 missing values generated)

{com}. gen byte newPM = 0
{txt}
{com}. replace newPM = 1 if financialyear == 1991 | financialyear == 1998 | financialyear == 1991 | /*
> */ financialyear == 2008 | financialyear == 2011
{txt}(273 real changes made)

{com}. gen L_newPM = L.newPM
{txt}(245 missing values generated)

{com}. gen newMIN = 1 if MinID ~= L.MinID & MinID ~= .
{txt}(927 missing values generated)

{com}. replace newMIN = 0 if MinID == L.MinID
{txt}(927 real changes made)

{com}. gen L_newMIN = L.newMIN
{txt}(245 missing values generated)

{com}. 
. * Generate financial year dummies
. sort agencyID financialyear
{txt}
{com}. save, replace
{txt}file workfile.dta saved

{com}. tab financialyear, gen(FY)

  {txt}financial {c |}
 year (1990 {c |}
    = 89/90 {c |}
       etc) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1989 {c |}{res}          3        0.16        0.16
{txt}       1990 {c |}{res}         12        0.65        0.81
{txt}       1991 {c |}{res}         33        1.79        2.60
{txt}       1992 {c |}{res}         54        2.93        5.53
{txt}       1993 {c |}{res}         69        3.74        9.27
{txt}       1994 {c |}{res}         83        4.50       13.77
{txt}       1995 {c |}{res}         89        4.83       18.60
{txt}       1996 {c |}{res}         96        5.21       23.81
{txt}       1997 {c |}{res}        111        6.02       29.83
{txt}       1998 {c |}{res}        105        5.69       35.52
{txt}       1999 {c |}{res}        107        5.80       41.32
{txt}       2000 {c |}{res}        104        5.64       46.96
{txt}       2001 {c |}{res}        102        5.53       52.49
{txt}       2002 {c |}{res}         97        5.26       57.75
{txt}       2003 {c |}{res}         94        5.10       62.85
{txt}       2004 {c |}{res}         95        5.15       68.00
{txt}       2005 {c |}{res}         92        4.99       72.99
{txt}       2006 {c |}{res}         90        4.88       77.87
{txt}       2007 {c |}{res}         84        4.56       82.43
{txt}       2008 {c |}{res}         75        4.07       86.50
{txt}       2009 {c |}{res}         64        3.47       89.97
{txt}       2010 {c |}{res}         68        3.69       93.66
{txt}       2011 {c |}{res}         60        3.25       96.91
{txt}       2012 {c |}{res}         57        3.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,844      100.00
{txt}
{com}. 
. * Generate failure indicator: 
. gen CEOdeparture = .
{txt}(1,844 missing values generated)

{com}. replace CEOdeparture = 1 if F1.succession == 1 | (agency_termination > 0 & /*
> */ agency_termination ~= .)
{txt}(341 real changes made)

{com}. replace CEOdeparture = 0 if F1.succession == 0
{txt}(1,380 real changes made)

{com}. save, replace
{txt}file workfile.dta saved

{com}. 
. * Generate agency-CEO duration indicator: 
. egen agencyCEOid = group(agencyID ceoid)
{txt}
{com}. gen ceoduration = 1 if succession == 1 | agencystatus == 2
{txt}(1,393 missing values generated)

{com}. gen LAGagencyCEOid = L.agencyCEOid
{txt}(245 missing values generated)

{com}. replace ceoduration = 2 if LAGagencyCEOid == agencyCEOid & (L.succession == 1 | L.agencystatus == 2)
{txt}(383 real changes made)

{com}. replace ceoduration = 3 if LAGagencyCEOid == agencyCEOid & (L2.succession == 1 | L2.agencystatus == 2) /*
> */ & L.succession == 0
{txt}(296 real changes made)

{com}. replace ceoduration = 4 if LAGagencyCEOid == agencyCEOid & (L3.succession == 1 | L3.agencystatus == 2) /*
> */ & L2.succession == 0 & L.succession == 0
{txt}(220 real changes made)

{com}. replace ceoduration = 5 if LAGagencyCEOid == agencyCEOid & (L4.succession == 1 | L4.agencystatus == 2) /*
> */ & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(157 real changes made)

{com}. replace ceoduration = 6 if LAGagencyCEOid == agencyCEOid & (L5.succession == 1 | L5.agencystatus == 2) /*
> */ & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(107 real changes made)

{com}. replace ceoduration = 7 if LAGagencyCEOid == agencyCEOid & (L6.succession == 1 | L6.agencystatus == 2) /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(72 real changes made)

{com}. replace ceoduration = 8 if LAGagencyCEOid == agencyCEOid & (L7.succession == 1 | L7.agencystatus == 2) /*
> */ & L6.succession == 0 /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(42 real changes made)

{com}. replace ceoduration = 9 if LAGagencyCEOid == agencyCEOid & (L8.succession == 1 | L8.agencystatus == 2) /*
> */ & L7.succession == 0 & L6.succession == 0 /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(31 real changes made)

{com}. replace ceoduration = 10 if LAGagencyCEOid == agencyCEOid & (L9.succession == 1 | L9.agencystatus == 2) /*
> */ & L8.succession == 0 & L7.succession == 0 & L6.succession == 0 /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(22 real changes made)

{com}. replace ceoduration = 11 if LAGagencyCEOid == agencyCEOid & (L10.succession == 1 | L10.agencystatus == 2) /*
> */ & L9.succession == 0 & L8.succession == 0 & L7.succession == 0 & L6.succession == 0 /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(14 real changes made)

{com}. replace ceoduration = 12 if LAGagencyCEOid == agencyCEOid & (L11.succession == 1 | L11.agencystatus == 2) /*
> */ & L10.succession == 0 & L9.succession == 0 & L8.succession == 0 & L7.succession == 0 & L6.succession == 0 /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(8 real changes made)

{com}. replace ceoduration = 13 if LAGagencyCEOid == agencyCEOid & (L12.succession == 1 | L12.agencystatus == 2) /*
> */ & L11.succession == 0 /*
> */ & L10.succession == 0 & L9.succession == 0 & L8.succession == 0 & L7.succession == 0 & L6.succession == 0 /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(4 real changes made)

{com}. replace ceoduration = 14 if LAGagencyCEOid == agencyCEOid & (L13.succession == 1 | L13.agencystatus == 2) /*
> */ & L12.succession == 0 & L11.succession == 0 /*
> */ & L10.succession == 0 & L9.succession == 0 & L8.succession == 0 & L7.succession == 0 & L6.succession == 0 /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(2 real changes made)

{com}. replace ceoduration = 15 if LAGagencyCEOid == agencyCEOid & (L14.succession == 1 | L14.agencystatus == 2) /*
> */ & L13.succession == 0 & L12.succession == 0 & L11.succession == 0 /*
> */ & L10.succession == 0 & L9.succession == 0 & L8.succession == 0 & L7.succession == 0 & L6.succession == 0 /*
> */ & L5.succession == 0 & L4.succession == 0 & L3.succession == 0 & L2.succession == 0 & L.succession == 0
{txt}(1 real change made)

{com}. drop if ceoduration == .
{txt}(37 observations deleted)

{com}. drop if ceoduration == L.ceoduration
{txt}(26 observations deleted)

{com}. compress
  {txt}variable {bf}advdegree{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}badegree{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}targsmiles_set{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}targsmiles_met{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}low_tarmil_FY{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}high_tarmil_FY{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}L_low_tarmil_FY{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}L_high_tarmil_FY{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}L_higher_score{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}L_lower_score{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}internal{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}external{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}regulatory{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}research{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}L_newparty2{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}L_newPM{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}newMIN{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}L_newMIN{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}CEOdeparture{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}agencyCEOid{sf} was {bf}{res}float{sf}{txt} now {bf}{res}int{sf}
  {txt}variable {bf}ceoduration{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}LAGagencyCEOid{sf} was {bf}{res}float{sf}{txt} now {bf}{res}int{sf}
{txt}  (113,984 bytes saved)

{com}. sort agencyID financialyear
{txt}
{com}. save, replace
{txt}file workfile.dta saved

{com}. 
. * Prior appointment sector dummies
. * (military are considered central government insiders)
. 
. replace ceocivil = 1 if ceomilitary == 1
{txt}(166 real changes made)

{com}. gen otherpublicorigin = 1 if (ceopublic == 1 & ceocivil == 0)
{txt}(1,594 missing values generated)

{com}. assert otherpublicorigin ~= 1 if ceocivil == 1 | ceopublic == 0
{txt}
{com}. replace otherpublicorigin = 0 if ceocivil == 1 | ceopublic == 0
{txt}(1,589 real changes made)

{com}. gen privateorigin = 1 if (ceoprivate_profit == 1 | ceononprofit == 1)
{txt}(1,509 missing values generated)

{com}. assert privateorigin ~= 1 if (ceoprivate_profit == 0 & ceononprofit == 0)
{txt}
{com}. replace privateorigin = 0 if (ceoprivate_profit == 0 & ceononprofit == 0)
{txt}(1,504 real changes made)

{com}. 
. * Cross-tabs
. tab ceocivil otherpublicorigin

 {txt}Was CEO's {c |}
  previous {c |}
   post in {c |}
 the civil {c |}   otherpublicorigin
  service? {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        No {c |}{res}       275        187 {txt}{c |}{res}       462 
{txt}       Yes {c |}{res}     1,314          0 {txt}{c |}{res}     1,314 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,589        187 {txt}{c |}{res}     1,776 

{txt}
{com}. tab ceocivil privateorigin

 {txt}Was CEO's {c |}
  previous {c |}
   post in {c |}
 the civil {c |}     privateorigin
  service? {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        No {c |}{res}       190        272 {txt}{c |}{res}       462 
{txt}       Yes {c |}{res}     1,314          0 {txt}{c |}{res}     1,314 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,504        272 {txt}{c |}{res}     1,776 

{txt}
{com}. tab otherpublicorigin privateorigin

{txt}otherpubli {c |}     privateorigin
   corigin {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}     1,317        272 {txt}{c |}{res}     1,589 
{txt}         1 {c |}{res}       187          0 {txt}{c |}{res}       187 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,504        272 {txt}{c |}{res}     1,776 

{txt}
{com}. 
. * Destination sector variables
. gen civpubnotpub = 0 if CEOdeparture == 0
{txt}(446 missing values generated)

{com}. replace civpubnotpub = 1 if CEOdeparture == 1 & destcivil == 1 
{txt}(103 real changes made)

{com}. replace civpubnotpub = 2 if CEOdeparture == 1 & destpubnotcivil == 1
{txt}(43 real changes made)

{com}. replace civpubnotpub = 3 if (CEOdeparture == 1 & destcivil == 0 & /*
> */ destpubnotcivil == 0 & destret == 0)
{txt}(77 real changes made)

{com}. replace civpubnotpub = 4 if CEOdeparture == 1 & destret == 1
{txt}(91 real changes made)

{com}. assert destcivil ~= 1 if destret == 1
{txt}
{com}. assert destpubnotcivil ~= 1 if destret == 1
{txt}
{com}. gen employedretired = 0 if civpubnotpub == 0
{txt}(446 missing values generated)

{com}. replace employedretired = 1 if civpubnotpub > 0 & civpubnotpub < 4
{txt}(223 real changes made)

{com}. replace employedretired = 2 if civpubnotpub == 4
{txt}(91 real changes made)

{com}. 
. * Exits to the military are considered exits into central government
. replace civpubnotpub = 1 if pubsectordestination == 1
{txt}(57 real changes made)

{com}. 
. * Ensure consistency of tenure ending variables
. replace CEOdeparture = . if destcivil == .
{txt}(36 real changes made, 36 to missing)

{com}. save, replace
{txt}file workfile.dta saved

{com}. 
. * Generate agency termination indicator
. gen anytermination =0
{txt}
{com}. replace anytermination =1 if agency_termination > 0 
{txt}(118 real changes made)

{com}. tab anytermination agency_termination

{txt}anytermina {c |}   if an organisation was terminated, what form of termination did it take?
      tion {c |} not termi  terminati  privatisa  change of  acquisiti  merger to  replaced  {c |}     Total
{hline 11}{c +}{hline 77}{c +}{hline 10}
         0 {c |}{res}     1,663          0          0          0          0          0          0 {txt}{c |}{res}     1,663 
{txt}         1 {c |}{res}         0          2         14         14         40         45          3 {txt}{c |}{res}       118 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
     Total {c |}{res}     1,663          2         14         14         40         45          3 {txt}{c |}{res}     1,781 

{txt}
{com}. 
. * Remove CEO spells that end with agency termination
. sort agencyCEOid
{txt}
{com}. by agencyCEOid: egen agencydeathmean = total(anytermination)
{txt}
{com}. sum agencydeathmean, d

                       {txt}agencydeathmean
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,781
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}      1,781

{txt}50%    {res}        0                      {txt}Mean          {res} .2599663
                        {txt}Largest       Std. Dev.     {res}  .438739
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1924919
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.094505
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.197942
{txt}
{com}. * How many "final CEO" spells? 
. codebook agencyCEOid if agencydeathmean ~= 0 & agencydeathmean ~= .

{txt}{hline}
{res}agencyCEOid{right:group(agencyID ceoid)}
{txt}{hline}

{col 19}type:  numeric ({res}int{txt})

{col 18}range:  [{res}1{txt},{res}464{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}118{col 51}{txt}missing .:  {res}0{txt}/{res}463

{txt}{col 19}mean:{res}{col 26} 214.281
{txt}{col 15}std. dev:{res}{col 26} 131.662

{txt}{col 12}percentiles:{col 32}10%{col 42}25%{col 52}50%{col 62}75%{col 72}90%
{res}{col 27}      41{col 37}      91{col 47}     205{col 57}     323{col 67}     404
{txt}
{com}. drop if agencydeathmean ~= 0
{txt}(463 observations deleted)

{com}. sort agencyID financialyear
{txt}
{com}. save, replace
{txt}file workfile.dta saved

{com}. 
. * Update the tenure ending variable to combine 
. * exit into central government and exit into other parts of the 
. * public sector
. gen civpubnotpub2 = civpubnotpub
{txt}(128 missing values generated)

{com}. replace civpubnotpub2 = 1 if civpubnotpub2 == 2
{txt}(12 real changes made)

{com}. tab civpubnotpub2 civpubnotpub

{txt}civpubnotp {c |}                      civpubnotpub
       ub2 {c |}         0          1          2          3          4 {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}       968          0          0          0          0 {txt}{c |}{res}       968 
{txt}         1 {c |}{res}         0         80         12          0          0 {txt}{c |}{res}        92 
{txt}         3 {c |}{res}         0          0          0         55          0 {txt}{c |}{res}        55 
{txt}         4 {c |}{res}         0          0          0          0         75 {txt}{c |}{res}        75 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       968         80         12         55         75 {txt}{c |}{res}     1,190 

{txt}
{com}. drop civpubnotpub
{txt}
{com}. compress
  {txt}variable {bf}otherpublicorigin{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}privateorigin{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}employedretired{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}anytermination{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}agencydeathmean{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}civpubnotpub2{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
  {txt}variable {bf}ceoname{sf} was {bf}{res}str75{sf}{txt} now {bf}{res}str71{sf}
{txt}  (28,996 bytes saved)

{com}. save, replace
{txt}file workfile.dta saved

{com}. 
. * Generate % efficiency and outcome targets
. gen pc_effoutcome_targets = 100 * ((outcome_set + efficiency_set) /*
> */ / (input_set + process_set + output_set + outcome_set + efficiency_set))
{txt}(132 missing values generated)

{com}. gen L_pc_effoutcome_targets = L.pc_effoutcome_targets
{txt}(320 missing values generated)

{com}. 
. * The variable with the overall # of targets: targsmiles_set
. gen L_targsmiles_set = L.targsmiles_set
{txt}(249 missing values generated)

{com}. compress
  {txt}variable {bf}L_targsmiles_set{sf} was {bf}{res}float{sf}{txt} now {bf}{res}byte{sf}
{txt}  (3,954 bytes saved)

{com}. save, replace
{txt}file workfile.dta saved

{com}. 
. 
. * Table 3: all departures, without disaggregating by type of destination
. * Specification with high and low target achievement dummies
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 1, 3, 4)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 1 3 4
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}         21{txt}  observations begin on or after (first) failure
{hline 78}
{res}       1297{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}        201{txt}  failures in single-failure-per-subject data
{res}       1324{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcox ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score

         {txt}failure _d:  {res}civpubnotpub2 == 1 3 4
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:   log likelihood = {res}-633.81802
{txt}Iteration 1:   log likelihood = {res}-614.09108
{txt}Iteration 2:   log likelihood = {res}-613.33034
{txt}Iteration 3:   log likelihood = {res}-613.31877
{txt}Iteration 4:   log likelihood = {res}-613.31876
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-613.31876

{txt}Cox regression -- Breslow method for ties

No. of subjects = {res}         247                  {txt}Number of obs    =  {res}       934
{txt}No. of failures = {res}         142
{txt}Time at risk    = {res}         934
                                                {txt}LR chi2({res}18{txt})      =  {res}     41.00
{txt}Log likelihood  =   {res}-613.31876                  {txt}Prob > chi2      =  {res}    0.0015

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                     _t{col 25}{c |} Haz. Ratio{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ceocivil {c |}{col 25}{res}{space 2} 1.266187{col 37}{space 2} .2662887{col 48}{space 1}    1.12{col 57}{space 3}0.262{col 65}{space 4} .8384623{col 78}{space 3} 1.912106
{txt}{space 7}L_high_tarmil_FY {c |}{col 25}{res}{space 2} 1.169282{col 37}{space 2} .2671881{col 48}{space 1}    0.68{col 57}{space 3}0.494{col 65}{space 4} .7471611{col 78}{space 3} 1.829886
{txt}{space 8}L_low_tarmil_FY {c |}{col 25}{res}{space 2} 1.383253{col 37}{space 2} .3059936{col 48}{space 1}    1.47{col 57}{space 3}0.142{col 65}{space 4} .8966122{col 78}{space 3}  2.13402
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} .5566258{col 37}{space 2} .1935365{col 48}{space 1}   -1.68{col 57}{space 3}0.092{col 65}{space 4} .2815815{col 78}{space 3} 1.100329
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2} .8460672{col 37}{space 2} .2708634{col 48}{space 1}   -0.52{col 57}{space 3}0.602{col 65}{space 4} .4517495{col 78}{space 3} 1.584572
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} 1.100821{col 37}{space 2} .2027048{col 48}{space 1}    0.52{col 57}{space 3}0.602{col 65}{space 4} .7673208{col 78}{space 3} 1.579271
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2} .9303706{col 37}{space 2} .1783831{col 48}{space 1}   -0.38{col 57}{space 3}0.707{col 65}{space 4} .6389284{col 78}{space 3} 1.354752
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} .8966563{col 37}{space 2} .1970085{col 48}{space 1}   -0.50{col 57}{space 3}0.620{col 65}{space 4} .5829142{col 78}{space 3} 1.379264
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2} 1.842541{col 37}{space 2} .3974188{col 48}{space 1}    2.83{col 57}{space 3}0.005{col 65}{space 4} 1.207316{col 78}{space 3} 2.811987
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2} 1.073874{col 37}{space 2} .0228199{col 48}{space 1}    3.35{col 57}{space 3}0.001{col 65}{space 4} 1.030066{col 78}{space 3} 1.119545
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2} .7255678{col 37}{space 2} .2460224{col 48}{space 1}   -0.95{col 57}{space 3}0.344{col 65}{space 4} .3732985{col 78}{space 3} 1.410262
{txt}{space 18}staff {c |}{col 25}{res}{space 2}  1.00002{col 37}{space 2} 9.06e-06{col 48}{space 1}    2.15{col 57}{space 3}0.031{col 65}{space 4} 1.000002{col 78}{space 3} 1.000037
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} .8320978{col 37}{space 2} .1935572{col 48}{space 1}   -0.79{col 57}{space 3}0.429{col 65}{space 4} .5274404{col 78}{space 3}  1.31273
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2} .8765461{col 37}{space 2} .2036061{col 48}{space 1}   -0.57{col 57}{space 3}0.571{col 65}{space 4} .5559756{col 78}{space 3} 1.381954
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2}  .987582{col 37}{space 2} .0075068{col 48}{space 1}   -1.64{col 57}{space 3}0.100{col 65}{space 4}  .972978{col 78}{space 3} 1.002405
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2} .9886794{col 37}{space 2}  .014177{col 48}{space 1}   -0.79{col 57}{space 3}0.427{col 65}{space 4} .9612799{col 78}{space 3}  1.01686
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2} .8650138{col 37}{space 2} .0931873{col 48}{space 1}   -1.35{col 57}{space 3}0.178{col 65}{space 4} .7003638{col 78}{space 3} 1.068372
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2}  1.05097{col 37}{space 2} .1034954{col 48}{space 1}    0.50{col 57}{space 3}0.614{col 65}{space 4} .8664975{col 78}{space 3} 1.274716
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * outreg2 using Table3.doc, eform tstat bdec(2) tdec(2) word replace
. gen coxsample = 1 if e(sample)
{txt}(384 missing values generated)

{com}. 
. * Plot hazard function with all explanatory variables held at the mean
. stcurve, hazard
{res}{txt}
{com}. graph export stcurve3a.png, replace
{txt}(file stcurve3a.png written in PNG format)

{com}. 
. * Plot hazard function for change in party control yes and no and 
. * with all other explanatory variables held at the mean
. stcurve, hazard at1(L_newparty2=0) at2(L_newparty2=1)
{res}{txt}
{com}. graph export stcurve3b.png, replace
{txt}(file stcurve3b.png written in PNG format)

{com}. 
. * Age comparison at retirement between Civil Service insiders and outsiders
. sum ceoage if e(sample) & civpubnotpub2 == 4 & ceocivil == 1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ceoage {c |}{res}         54    58.04801    2.780726   51.24709   63.60575
{txt}
{com}. sum ceoage if e(sample) & civpubnotpub2 == 4 & ceocivil == 0

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ceoage {c |}{res}          6    61.43829    3.188413   57.98494    66.2998
{txt}
{com}. ttest ceoage if e(sample) & civpubnotpub2 == 4, by(ceocivil)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}      6{col 22} 61.43829{col 34} 1.301664{col 46} 3.188413{col 58} 58.09225{col 70} 64.78432
     {txt}Yes {c |}{res}{col 12}     54{col 22} 58.04801{col 34} .3784088{col 46} 2.780726{col 58} 57.28902{col 70} 58.80701
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     60{col 22} 58.38704{col 34} .3842655{col 46} 2.976508{col 58} 57.61813{col 70} 59.15595
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} 3.390272{col 34} 1.212758{col 58} .9626725{col 70} 5.817871
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  2.7955
{txt}Ho: diff = 0                                     degrees of freedom = {res}      58

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9965         {txt}Pr(|T| > |t|) = {res}0.0070          {txt}Pr(T > t) = {res}0.0035
{txt}
{com}. 
. * Age comparison between retirements following low performance and other retirements
. sum ceoage if e(sample) & civpubnotpub2 == 4 & L_low_tarmil_FY == 1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ceoage {c |}{res}         16    56.91581    2.923964   51.24709   61.36071
{txt}
{com}. sum ceoage if e(sample) & civpubnotpub2 == 4 & L_low_tarmil_FY == 0

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ceoage {c |}{res}         44    58.92203    2.841744   51.51266    66.2998
{txt}
{com}. ttest ceoage if e(sample) & civpubnotpub2 == 4, by(L_low_tarmil_FY)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}     44{col 22} 58.92203{col 34}  .428409{col 46} 2.841744{col 58} 58.05806{col 70}   59.786
       {txt}1 {c |}{res}{col 12}     16{col 22} 56.91581{col 34} .7309911{col 46} 2.923964{col 58} 55.35774{col 70} 58.47388
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     60{col 22} 58.38704{col 34} .3842655{col 46} 2.976508{col 58} 57.61813{col 70} 59.15595
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} 2.006223{col 34} .8358843{col 58} .3330184{col 70} 3.679427
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  2.4001
{txt}Ho: diff = 0                                     degrees of freedom = {res}      58

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9902         {txt}Pr(|T| > |t|) = {res}0.0196          {txt}Pr(T > t) = {res}0.0098
{txt}
{com}. 
. 
. * Table 1: Summary statistics for the estimation sample
. gen L_mediastories = L.mediastories
{txt}(211 missing values generated)

{com}. sum ceoduration /*
> */ ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ L_mediastories /*
> */ media_Z_score /*
> */ mediastories /*
> */ if coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}        934
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        4                      {txt}Mean          {res} 4.069593
                        {txt}Largest       Std. Dev.     {res} 2.453529
{txt}75%    {res}        5             13
{txt}90%    {res}        7             13       {txt}Variance      {res} 6.019803
{txt}95%    {res}        9             13       {txt}Skewness      {res} 1.046561
{txt}99%    {res}       12             14       {txt}Kurtosis      {res} 3.950072

        {txt}Was CEO's previous post in the civil service?
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        1                      {txt}Mean          {res} .7034261
                        {txt}Largest       Std. Dev.     {res} .4569917
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .2088414
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.8907607
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.793455

                      {txt}L_high_tarmil_FY
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .1862955
                        {txt}Largest       Std. Dev.     {res} .3895535
{txt}75%    {res}        0              1
{txt}90%    {res}        1              1       {txt}Variance      {res}  .151752
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.611448
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.596763

                       {txt}L_low_tarmil_FY
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .1755889
                        {txt}Largest       Std. Dev.     {res} .3806738
{txt}75%    {res}        0              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1449126
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.705318
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.908109

                         {txt}L_newparty2
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .1070664
                        {txt}Largest       Std. Dev.     {res} .3093633
{txt}75%    {res}        0              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0957056
{txt}95%    {res}        1              1       {txt}Skewness      {res} 2.541634
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 7.459904

                          {txt}newparty2
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .1059957
                        {txt}Largest       Std. Dev.     {res} .3079971
{txt}75%    {res}        0              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0948622
{txt}95%    {res}        1              1       {txt}Skewness      {res} 2.559865
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 7.552906

                          {txt}L_newMIN
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .4957173
                        {txt}Largest       Std. Dev.     {res} .5002495
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .2502496
{txt}95%    {res}        1              1       {txt}Skewness      {res} .0171312
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.000293

                           {txt}newMIN
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .4218415
                        {txt}Largest       Std. Dev.     {res} .4941181
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .2441527
{txt}95%    {res}        1              1       {txt}Skewness      {res} .3165249
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.100188

                          {txt}badegree
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        1              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        1                      {txt}Mean          {res} .7687366
                        {txt}Largest       Std. Dev.     {res} .4218663
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1779712
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.274719
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}  2.62491

                 {txt}Was CEO Oxbridge educated?
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .2087794
                        {txt}Largest       Std. Dev.     {res} .4066542
{txt}75%    {res}        0              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1653676
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.433044
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.053614

                           {txt}ceoage
{hline 61}
      Percentiles      Smallest
 1%    {res} 40.41341       34.60917
{txt} 5%    {res} 44.01095       35.60849
{txt}10%    {res} 46.09172       36.61054       {txt}Obs         {res}        934
{txt}25%    {res} 49.71663       37.60986       {txt}Sum of Wgt. {res}        934

{txt}50%    {res} 53.46475                      {txt}Mean          {res}  53.1602
                        {txt}Largest       Std. Dev.     {res} 5.174769
{txt}75%    {res} 57.04038       64.29842
{txt}90%    {res} 59.46338        64.4627       {txt}Variance      {res} 26.77823
{txt}95%    {res} 60.82135       65.29774       {txt}Skewness      {res}-.3732063
{txt}99%    {res} 63.35113        66.2998       {txt}Kurtosis      {res} 2.898544

                        {txt}gender of ceo
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res}  .117773
                        {txt}Largest       Std. Dev.     {res} .3225119
{txt}75%    {res}        0              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1040139
{txt}95%    {res}        1              1       {txt}Skewness      {res} 2.371583
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 6.624404

         {txt}no of staff emplyed in agency in this year
{hline 61}
      Percentiles      Smallest
 1%    {res}       40             30
{txt} 5%    {res}       60             30
{txt}10%    {res}      100             30       {txt}Obs         {res}        934
{txt}25%    {res}      196             30       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}      670                      {txt}Mean          {res} 2453.984
                        {txt}Largest       Std. Dev.     {res} 7278.707
{txt}75%    {res}     1638          67860
{txt}90%    {res}     4520          68251       {txt}Variance      {res} 5.30e+07
{txt}95%    {res}     7909          69230       {txt}Skewness      {res} 6.475925
{txt}99%    {res}    41940          72003       {txt}Kurtosis      {res} 50.01129

                         {txt}regulatory
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .2109208
                        {txt}Largest       Std. Dev.     {res} .4081808
{txt}75%    {res}        0              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1666116
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.417186
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.008417

                  {txt}is agency a trading fund?
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        0                      {txt}Mean          {res} .2526767
                        {txt}Largest       Std. Dev.     {res} .4347799
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1890336
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.138304
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.295736

                   {txt}L_pc_effoutcome_targets
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res} 11.11111                      {txt}Mean          {res} 13.58489
                        {txt}Largest       Std. Dev.     {res} 13.98416
{txt}75%    {res}       20            100
{txt}90%    {res} 33.33333            100       {txt}Variance      {res} 195.5568
{txt}95%    {res}       40            100       {txt}Skewness      {res} 1.994392
{txt}99%    {res} 57.14286            100       {txt}Kurtosis      {res} 10.68249

                      {txt}L_targsmiles_set
{hline 61}
      Percentiles      Smallest
 1%    {res}        2              1
{txt} 5%    {res}        4              1
{txt}10%    {res}        5              1       {txt}Obs         {res}        934
{txt}25%    {res}        6              1       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        8                      {txt}Mean          {res} 9.680942
                        {txt}Largest       Std. Dev.     {res} 6.781138
{txt}75%    {res}       11             48
{txt}90%    {res}       16             55       {txt}Variance      {res} 45.98384
{txt}95%    {res}       21             83       {txt}Skewness      {res} 4.343188
{txt}99%    {res}       33             84       {txt}Kurtosis      {res} 37.50865

                       {txt}L_media_Z_score
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.597948      -2.128979
{txt} 5%    {res}-1.217596      -1.974991
{txt}10%    {res}-.9963414      -1.868927       {txt}Obs         {res}        934
{txt}25%    {res}-.7069923      -1.845204       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}-.2954834                      {txt}Mean          {res}-.0621162
                        {txt}Largest       Std. Dev.     {res} .9075721
{txt}75%    {res} .4777229        2.84605
{txt}90%    {res} 1.192569       3.019272       {txt}Variance      {res} .8236872
{txt}95%    {res} 1.711974       3.330911       {txt}Skewness      {res} .9238782
{txt}99%    {res} 2.556983       4.319255       {txt}Kurtosis      {res} 3.913098

                       {txt}L_mediastories
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        3              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}       18                      {txt}Mean          {res} 81.49036
                        {txt}Largest       Std. Dev.     {res} 149.4733
{txt}75%    {res}       82            962
{txt}90%    {res}      276            977       {txt}Variance      {res} 22342.26
{txt}95%    {res}      410            993       {txt}Skewness      {res} 3.010956
{txt}99%    {res}      751            994       {txt}Kurtosis      {res} 13.67409

                        {txt}media_Z_score
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.456563      -1.974991
{txt} 5%    {res}-1.092082      -1.698216
{txt}10%    {res} -.918071      -1.597948       {txt}Obs         {res}        934
{txt}25%    {res}-.6423641      -1.590868       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}-.1825157                      {txt}Mean          {res} .0427371
                        {txt}Largest       Std. Dev.     {res} .9092813
{txt}75%    {res} .6396597       2.844755
{txt}90%    {res} 1.312749       3.019272       {txt}Variance      {res} .8267926
{txt}95%    {res} 1.788854       3.330911       {txt}Skewness      {res}  .869996
{txt}99%    {res} 2.585355       4.319255       {txt}Kurtosis      {res} 3.593174

     {txt}Number of media stories about the agency this year
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        934
{txt}25%    {res}        4              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}       19                      {txt}Mean          {res} 86.60278
                        {txt}Largest       Std. Dev.     {res} 158.1956
{txt}75%    {res}       87            977
{txt}90%    {res}      291            993       {txt}Variance      {res} 25025.85
{txt}95%    {res}      422            994       {txt}Skewness      {res} 3.016688
{txt}99%    {res}      863            994       {txt}Kurtosis      {res}  13.5377
{txt}
{com}. 
. sum L_targsmilespct_met if coxsample == 1, d

                     {txt}L_targsmilespct_met
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.66667              0
{txt} 5%    {res}     37.5              0
{txt}10%    {res}       50              0       {txt}Obs         {res}        934
{txt}25%    {res} 63.63636              0       {txt}Sum of Wgt. {res}        934

{txt}50%    {res} 81.53409                      {txt}Mean          {res} 77.47716
                        {txt}Largest       Std. Dev.     {res} 21.18696
{txt}75%    {res}      100            100
{txt}90%    {res}      100            100       {txt}Variance      {res} 448.8873
{txt}95%    {res}      100            100       {txt}Skewness      {res}-.9601904
{txt}99%    {res}      100            100       {txt}Kurtosis      {res} 3.648225
{txt}
{com}. 
. sum L_targsmiles_set if coxsample == 1, d

                      {txt}L_targsmiles_set
{hline 61}
      Percentiles      Smallest
 1%    {res}        2              1
{txt} 5%    {res}        4              1
{txt}10%    {res}        5              1       {txt}Obs         {res}        934
{txt}25%    {res}        6              1       {txt}Sum of Wgt. {res}        934

{txt}50%    {res}        8                      {txt}Mean          {res} 9.680942
                        {txt}Largest       Std. Dev.     {res} 6.781138
{txt}75%    {res}       11             48
{txt}90%    {res}       16             55       {txt}Variance      {res} 45.98384
{txt}95%    {res}       21             83       {txt}Skewness      {res} 4.343188
{txt}99%    {res}       33             84       {txt}Kurtosis      {res} 37.50865
{txt}
{com}. list agencyname financialyear L_targsmiles_set if /*
> */ L_targsmiles_set < 3 & coxsample == 1
{txt}
      {c TLC}{hline 31}{c -}{hline 10}{c -}{hline 10}{c TRC}
      {c |} {res}                   agencyname   financ~r   L_ta~set {txt}{c |}
      {c LT}{hline 31}{c -}{hline 10}{c -}{hline 10}{c RT}
  56. {c |} {res}Central Office of Information       2004          2 {txt}{c |}
 376. {c |} {res}         Fire Service College       2007          2 {txt}{c |}
 382. {c |} {res}  Fisheries Research Services       1999          1 {txt}{c |}
 960. {c |} {res}                   Royal Mint       1994          1 {txt}{c |}
 961. {c |} {res}                   Royal Mint       1995          1 {txt}{c |}
      {c LT}{hline 31}{c -}{hline 10}{c -}{hline 10}{c RT}
 962. {c |} {res}                   Royal Mint       1996          1 {txt}{c |}
 963. {c |} {res}                   Royal Mint       1997          1 {txt}{c |}
 964. {c |} {res}                   Royal Mint       1998          1 {txt}{c |}
1205. {c |} {res}           UK Passport Agency       1999          2 {txt}{c |}
1284. {c |} {res}     Warren Spring Laboratory       1991          1 {txt}{c |}
      {c LT}{hline 31}{c -}{hline 10}{c -}{hline 10}{c RT}
1285. {c |} {res}     Warren Spring Laboratory       1992          2 {txt}{c |}
1303. {c |} {res}Wilton Park Conference Centre       2011          2 {txt}{c |}
1304. {c |} {res}Wilton Park Conference Centre       2012          2 {txt}{c |}
1311. {c |} {res}          Disclosure Scotland       2011          1 {txt}{c |}
1312. {c |} {res}          Disclosure Scotland       2012          1 {txt}{c |}
      {c BLC}{hline 31}{c -}{hline 10}{c -}{hline 10}{c BRC}

{com}. 
. * mkcorr ceoduration /*
> */ ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ L_mediastories /*
> */ media_Z_score /*
> */ mediastories /*
> */ if coxsample == 1, log(Table1sumStats.txt) replace /*
> */ casewise means nocorr mdec(2)
. 
. 
. * Figure 1: Survivor function for estimation sample
. sts graph if coxsample == 1, title("")

         {txt}failure _d:  {res}civpubnotpub2 == 1 3 4
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid
{txt}
{com}. * graph export Figure1.png, replace
. 
. * How many agencies are in the estimation sample? 
. codebook agencyID if coxsample == 1

{txt}{hline}
{res}agencyID{right:our id number for agency}
{txt}{hline}

{col 19}type:  numeric ({res}int{txt})

{col 18}range:  [{res}4{txt},{res}216{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}129{col 51}{txt}missing .:  {res}0{txt}/{res}934

{txt}{col 19}mean:{res}{col 26} 113.188
{txt}{col 15}std. dev:{res}{col 26} 60.8871

{txt}{col 12}percentiles:{col 32}10%{col 42}25%{col 52}50%{col 62}75%{col 72}90%
{res}{col 27}      24{col 37}      64{col 47}     115{col 57}     166{col 67}     197
{txt}
{com}. 
. 
. * Endnote 10 (also referred to in endnote 8): "We tested 
. * the appropriateness of the proportional hazards assumption 
. * required by our models. To do so, we included interactions 
. * of time with all time-varying covariates. The null 
. * hypothesis was not rejected for any of these interactions."
. *****
. 
. * Tests of proportional hazards assumption
. stcox ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score, /*
> */ tvc(L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN /*
> */ ceoage /*
> */ staff /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score)

         {txt}failure _d:  {res}civpubnotpub2 == 1 3 4
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:   log likelihood = {res}-633.81802
{txt}Iteration 1:   log likelihood = {res}-611.15514
{txt}Iteration 2:   log likelihood = {res} -609.7146
{txt}Iteration 3:   log likelihood = {res}-609.67403
{txt}Iteration 4:   log likelihood = {res}-609.67389
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-609.67389

{txt}Cox regression -- Breslow method for ties

No. of subjects = {res}         247                  {txt}Number of obs    =  {res}       934
{txt}No. of failures = {res}         142
{txt}Time at risk    = {res}         934
                                                {txt}LR chi2({res}31{txt})      =  {res}     48.29
{txt}Log likelihood  =   {res}-609.67389                  {txt}Prob > chi2      =  {res}    0.0247

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                     _t{col 25}{c |} Haz. Ratio{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}main                    {txt}{c |}
{space 15}ceocivil {c |}{col 25}{res}{space 2} 1.253547{col 37}{space 2} .2677621{col 48}{space 1}    1.06{col 57}{space 3}0.290{col 65}{space 4} .8247476{col 78}{space 3} 1.905287
{txt}{space 7}L_high_tarmil_FY {c |}{col 25}{res}{space 2} 1.152565{col 37}{space 2} .6505968{col 48}{space 1}    0.25{col 57}{space 3}0.801{col 65}{space 4} .3812249{col 78}{space 3} 3.484572
{txt}{space 8}L_low_tarmil_FY {c |}{col 25}{res}{space 2} 1.650504{col 37}{space 2} .7841796{col 48}{space 1}    1.05{col 57}{space 3}0.292{col 65}{space 4} .6504255{col 78}{space 3} 4.188277
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} 1.110877{col 37}{space 2} .8810947{col 48}{space 1}    0.13{col 57}{space 3}0.895{col 65}{space 4} .2347112{col 78}{space 3} 5.257731
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2}  .550989{col 37}{space 2} .4552066{col 48}{space 1}   -0.72{col 57}{space 3}0.471{col 65}{space 4} .1091219{col 78}{space 3} 2.782108
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} 1.338444{col 37}{space 2} .5522432{col 48}{space 1}    0.71{col 57}{space 3}0.480{col 65}{space 4} .5962022{col 78}{space 3} 3.004739
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2} .9247899{col 37}{space 2} .4120999{col 48}{space 1}   -0.18{col 57}{space 3}0.861{col 65}{space 4} .3861314{col 78}{space 3} 2.214884
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} .9079927{col 37}{space 2} .2046542{col 48}{space 1}   -0.43{col 57}{space 3}0.668{col 65}{space 4}  .583752{col 78}{space 3}  1.41233
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2}    1.862{col 37}{space 2}  .407876{col 48}{space 1}    2.84{col 57}{space 3}0.005{col 65}{space 4} 1.212053{col 78}{space 3} 2.860471
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2} 1.029442{col 37}{space 2} .0467165{col 48}{space 1}    0.64{col 57}{space 3}0.523{col 65}{space 4} .9418331{col 78}{space 3}   1.1252
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2} .6843526{col 37}{space 2} .2374324{col 48}{space 1}   -1.09{col 57}{space 3}0.274{col 65}{space 4}  .346705{col 78}{space 3} 1.350827
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000013{col 37}{space 2} .0000256{col 48}{space 1}    0.50{col 57}{space 3}0.615{col 65}{space 4} .9999628{col 78}{space 3} 1.000063
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} .8618332{col 37}{space 2} .2050069{col 48}{space 1}   -0.63{col 57}{space 3}0.532{col 65}{space 4} .5406862{col 78}{space 3} 1.373729
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2}  .915199{col 37}{space 2} .5168853{col 48}{space 1}   -0.16{col 57}{space 3}0.875{col 65}{space 4} .3025343{col 78}{space 3} 2.768576
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2} .9667394{col 37}{space 2} .0187228{col 48}{space 1}   -1.75{col 57}{space 3}0.081{col 65}{space 4} .9307312{col 78}{space 3} 1.004141
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2} 1.012295{col 37}{space 2} .0331623{col 48}{space 1}    0.37{col 57}{space 3}0.709{col 65}{space 4}  .949341{col 78}{space 3} 1.079424
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2} .9743395{col 37}{space 2} .2433053{col 48}{space 1}   -0.10{col 57}{space 3}0.917{col 65}{space 4} .5972472{col 78}{space 3} 1.589522
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} .7706232{col 37}{space 2} .1922395{col 48}{space 1}   -1.04{col 57}{space 3}0.296{col 65}{space 4} .4726085{col 78}{space 3} 1.256558
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}tvc                     {txt}{c |}
{space 7}L_high_tarmil_FY {c |}{col 25}{res}{space 2} 1.001434{col 37}{space 2} .1088991{col 48}{space 1}    0.01{col 57}{space 3}0.989{col 65}{space 4} .8092079{col 78}{space 3} 1.239324
{txt}{space 8}L_low_tarmil_FY {c |}{col 25}{res}{space 2} .9564189{col 37}{space 2}  .084363{col 48}{space 1}   -0.51{col 57}{space 3}0.613{col 65}{space 4}  .804574{col 78}{space 3} 1.136921
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} .8690847{col 37}{space 2} .1412112{col 48}{space 1}   -0.86{col 57}{space 3}0.388{col 65}{space 4} .6320576{col 78}{space 3} 1.194999
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2}  1.09014{col 37}{space 2} .1615173{col 48}{space 1}    0.58{col 57}{space 3}0.560{col 65}{space 4} .8153921{col 78}{space 3} 1.457464
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} .9646036{col 37}{space 2}  .074315{col 48}{space 1}   -0.47{col 57}{space 3}0.640{col 65}{space 4} .8294126{col 78}{space 3}  1.12183
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2} 1.004163{col 37}{space 2} .0876059{col 48}{space 1}    0.05{col 57}{space 3}0.962{col 65}{space 4} .8463365{col 78}{space 3} 1.191421
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2} 1.010605{col 37}{space 2} .0096172{col 48}{space 1}    1.11{col 57}{space 3}0.268{col 65}{space 4}   .99193{col 78}{space 3} 1.029631
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000001{col 37}{space 2} 6.26e-06{col 48}{space 1}    0.22{col 57}{space 3}0.822{col 65}{space 4} .9999891{col 78}{space 3} 1.000014
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2} .9845511{col 37}{space 2} .0994401{col 48}{space 1}   -0.15{col 57}{space 3}0.877{col 65}{space 4} .8077305{col 78}{space 3}  1.20008
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2} 1.004919{col 37}{space 2} .0038779{col 48}{space 1}    1.27{col 57}{space 3}0.203{col 65}{space 4} .9973474{col 78}{space 3} 1.012549
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2} .9949321{col 37}{space 2} .0065572{col 48}{space 1}   -0.77{col 57}{space 3}0.441{col 65}{space 4} .9821627{col 78}{space 3} 1.007867
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2}  .978052{col 37}{space 2} .0454515{col 48}{space 1}   -0.48{col 57}{space 3}0.633{col 65}{space 4} .8929053{col 78}{space 3} 1.071318
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} 1.066235{col 37}{space 2} .0486631{col 48}{space 1}    1.41{col 57}{space 3}0.160{col 65}{space 4} .9749988{col 78}{space 3} 1.166009
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: Variables in {res}tvc{txt} equation interacted with _t.{p_end}

{com}. 
. 
. * Descriptive information to help with the interpretation
. 
. * (i) What percentage of Civil Service insiders were also insiders to 
. * the agency they head?  
. tab ceoinside if CEOdeparture == 1 & ceocivil == 1 & coxsample == 1

  {txt}Was CEO's {c |}
 prior post {c |}
 inside the {c |}
    agency? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         No {c |}{res}         58       55.24       55.24
{txt}        Yes {c |}{res}         47       44.76      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        105      100.00
{txt}
{com}. 
. * (ii) What is the correlation by % target achievement and media attention?  
. pwcorr L_targsmilespct_met L_media_Z_score media_Z_score if coxsample == 1, sig

             {txt}{c |} L_ta~met L_medi~e media_~e
{hline 13}{c +}{hline 27}
L_targsm~met {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
L_media_Z_~e {c |} {res}  0.0162   1.0000 
             {txt}{c |}{res}   0.6215
             {txt}{c |}
media_Z_sc~e {c |} {res}  0.0359   0.3833   1.0000 
             {txt}{c |}{res}   0.2728   0.0000
             {txt}{c |}

{com}. 
. * (iii) What is the distribution of CEO age for Civil Service insiders who 
. * exit into formal retirement?  
. sum ceoage if civpubnotpub2 == 4 & ceocivil == 1 & coxsample == 1, d

                           {txt}ceoage
{hline 61}
      Percentiles      Smallest
 1%    {res} 51.24709       51.24709
{txt} 5%    {res} 52.34223       51.51266
{txt}10%    {res} 54.19028       52.34223       {txt}Obs         {res}         54
{txt}25%    {res} 55.96714       53.24846       {txt}Sum of Wgt. {res}         54

{txt}50%    {res}  59.3566                      {txt}Mean          {res} 58.04801
                        {txt}Largest       Std. Dev.     {res} 2.780726
{txt}75%    {res} 59.76728       61.52498
{txt}90%    {res} 60.76112       61.74127       {txt}Variance      {res} 7.732435
{txt}95%    {res} 61.74127       62.21492       {txt}Skewness      {res}-.6472392
{txt}99%    {res} 63.60575       63.60575       {txt}Kurtosis      {res} 2.796724
{txt}
{com}. 
. * (iv) Median durations by covariates
. 
. * Civil Service insiders:
. sum ceoduration if ceocivil == 1 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}        657
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}        657

{txt}50%    {res}        4                      {txt}Mean          {res} 4.086758
                        {txt}Largest       Std. Dev.     {res} 2.524289
{txt}75%    {res}        5             12
{txt}90%    {res}        8             13       {txt}Variance      {res} 6.372035
{txt}95%    {res}        9             13       {txt}Skewness      {res} 1.083556
{txt}99%    {res}       12             14       {txt}Kurtosis      {res}  3.96916
{txt}
{com}. 
. * Civil Service outsiders:
. sum ceoduration if ceocivil == 0 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}        277
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}        277

{txt}50%    {res}        4                      {txt}Mean          {res} 4.028881
                        {txt}Largest       Std. Dev.     {res} 2.280803
{txt}75%    {res}        5             10
{txt}90%    {res}        7             11       {txt}Variance      {res} 5.202061
{txt}95%    {res}        8             11       {txt}Skewness      {res} .9023507
{txt}99%    {res}       11             13       {txt}Kurtosis      {res}  3.69848
{txt}
{com}. 
. * Women: 
. sum ceoduration if ceofemale == 1 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}        110
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}        110

{txt}50%    {res}        3                      {txt}Mean          {res} 3.727273
                        {txt}Largest       Std. Dev.     {res} 2.294425
{txt}75%    {res}        5              9
{txt}90%    {res}        7             10       {txt}Variance      {res} 5.264387
{txt}95%    {res}        9             10       {txt}Skewness      {res} 1.064959
{txt}99%    {res}       10             11       {txt}Kurtosis      {res} 3.692168
{txt}
{com}. 
. * Men: 
. sum ceoduration if ceofemale == 0 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}        824
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}        824

{txt}50%    {res}        4                      {txt}Mean          {res} 4.115291
                        {txt}Largest       Std. Dev.     {res} 2.471723
{txt}75%    {res}        5             13
{txt}90%    {res}        8             13       {txt}Variance      {res} 6.109414
{txt}95%    {res}        9             13       {txt}Skewness      {res} 1.040439
{txt}99%    {res}       12             14       {txt}Kurtosis      {res} 3.958079
{txt}
{com}. 
. * Research agencies: 
. sum ceoduration if research == 1 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        2              1
{txt}10%    {res}        2              2       {txt}Obs         {res}         72
{txt}25%    {res}        3              2       {txt}Sum of Wgt. {res}         72

{txt}50%    {res}        5                      {txt}Mean          {res} 5.083333
                        {txt}Largest       Std. Dev.     {res} 2.587892
{txt}75%    {res}        7             10
{txt}90%    {res}        9             10       {txt}Variance      {res} 6.697183
{txt}95%    {res}       10             11       {txt}Skewness      {res} .6048308
{txt}99%    {res}       12             12       {txt}Kurtosis      {res} 2.642382
{txt}
{com}. 
. * All non-research agencies: 
. sum ceoduration if research == 0 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}        862
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}        862

{txt}50%    {res}        3                      {txt}Mean          {res} 3.984919
                        {txt}Largest       Std. Dev.     {res} 2.424422
{txt}75%    {res}        5             13
{txt}90%    {res}        7             13       {txt}Variance      {res} 5.877821
{txt}95%    {res}        9             13       {txt}Skewness      {res} 1.096031
{txt}99%    {res}       12             14       {txt}Kurtosis      {res}  4.15458
{txt}
{com}. 
. * (v) 
. * Figure 2
. * (composite figure containing the distribution of CEO duration 
. * for each of the competing exit types)
. * Figure panel 1: destination public sector
. histogram ceoduration if civpubnotpub2 == 1 & coxsample == 1, xscale(range(1 14))
{txt}(bin={res}6{txt}, start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. * graph export Figure2Panel1.png, replace
. sum ceoduration if civpubnotpub2 == 1 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}         39
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}         39

{txt}50%    {res}        4                      {txt}Mean          {res} 3.666667
                        {txt}Largest       Std. Dev.     {res} 1.675416
{txt}75%    {res}        5              6
{txt}90%    {res}        6              6       {txt}Variance      {res} 2.807018
{txt}95%    {res}        7              7       {txt}Skewness      {res} .2330496
{txt}99%    {res}        7              7       {txt}Kurtosis      {res} 2.256211
{txt}
{com}. * Figure panel 2: destination private and nonprofit sector
. histogram ceoduration if civpubnotpub2 == 3 & coxsample == 1, xscale(range(1 14))
{txt}(bin={res}6{txt}, start={res}1{txt}, width={res}1.8333333{txt})
{res}{txt}
{com}. * graph export Figure2Panel2.png, replace
. sum ceoduration if civpubnotpub2 == 3 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        2              2
{txt}10%    {res}        2              2       {txt}Obs         {res}         43
{txt}25%    {res}        3              2       {txt}Sum of Wgt. {res}         43

{txt}50%    {res}        4                      {txt}Mean          {res} 4.860465
                        {txt}Largest       Std. Dev.     {res}  2.55027
{txt}75%    {res}        6              9
{txt}90%    {res}        9             10       {txt}Variance      {res} 6.503876
{txt}95%    {res}       10             10       {txt}Skewness      {res} .8634307
{txt}99%    {res}       12             12       {txt}Kurtosis      {res} 3.172861
{txt}
{com}. * Figure panel 3: destination retirement
. histogram ceoduration if civpubnotpub2 == 4 & coxsample == 1, xscale(range(1 14))
{txt}(bin={res}7{txt}, start={res}2{txt}, width={res}1.7142857{txt})
{res}{txt}
{com}. * graph export Figure2Panel3.png, replace
. sum ceoduration if civpubnotpub2 == 4 & coxsample == 1, d

                         {txt}ceoduration
{hline 61}
      Percentiles      Smallest
 1%    {res}        2              2
{txt} 5%    {res}        2              2
{txt}10%    {res}        2              2       {txt}Obs         {res}         60
{txt}25%    {res}        4              2       {txt}Sum of Wgt. {res}         60

{txt}50%    {res}        5                      {txt}Mean          {res} 5.983333
                        {txt}Largest       Std. Dev.     {res} 2.999953
{txt}75%    {res}      7.5             12
{txt}90%    {res}       10             13       {txt}Variance      {res} 8.999718
{txt}95%    {res}     12.5             13       {txt}Skewness      {res} .7948554
{txt}99%    {res}       14             14       {txt}Kurtosis      {res}  3.20784
{txt}
{com}. 
. * (vi) 
. * Table 2: Transition matrix to help interpret finding on Civil Service origin
. gen int originsector = 1 if ceocivil == 1
{txt}(357 missing values generated)

{com}. replace originsector = 2 if otherpublicorigin == 1 | privateorigin == 1
{txt}(352 real changes made)

{com}. tab originsector civpubnotpub2 if civpubnotpub2 > 0 & /*
> */ civpubnotpub2 <= 4 & coxsample == 1, row chi2
{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

originsect {c |}          civpubnotpub2
        or {c |}         1          3          4 {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         1 {c |}{res}        33         19         54 {txt}{c |}{res}       106 
           {txt}{c |}{res}     31.13      17.92      50.94 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
         2 {c |}{res}         6         24          6 {txt}{c |}{res}        36 
           {txt}{c |}{res}     16.67      66.67      16.67 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}        39         43         60 {txt}{c |}{res}       142 
           {txt}{c |}{res}     27.46      30.28      42.25 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 30.6035  {txt} Pr = {res}0.000
{txt}
{com}. 
. * Table 4: sector competing risks
. * Specification with high and low target achievement dummies
. 
. * Destination: public sector
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 1)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 1
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}         21{txt}  observations begin on or after (first) failure
{hline 78}
{res}       1297{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         71{txt}  failures in single-failure-per-subject data
{res}       1324{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score if coxsample == 1, /*
> */ compete(civpubnotpub2 == 3, 4)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 1
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-181.69542}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-181.14256}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-181.13914}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-181.13914}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 1{txt}{col 50}No. failed{col 67}={col 69}{res}        39
{txt}Competing events{col 17}: {res}civpubno~2 == 3 4{txt}{col 50}No. competing{col 67}={col 69}{res}       103
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{txt}Wald chi2({res}18{txt}){col 67}={col 70}{res}    36.30
{txt}Log pseudolikelihood = {res}-181.13914{col 50}{txt}Prob > chi2{col 67}={col 73}{res}0.0064

{txt}{ralign 89:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                     _t{col 25}{c |}        SHR{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ceocivil {c |}{col 25}{res}{space 2} 2.951098{col 37}{space 2} 1.251053{col 48}{space 1}    2.55{col 57}{space 3}0.011{col 65}{space 4} 1.285687{col 78}{space 3} 6.773792
{txt}{space 7}L_high_tarmil_FY {c |}{col 25}{res}{space 2} .2927312{col 37}{space 2} .2096561{col 48}{space 1}   -1.72{col 57}{space 3}0.086{col 65}{space 4} .0719172{col 78}{space 3} 1.191531
{txt}{space 8}L_low_tarmil_FY {c |}{col 25}{res}{space 2} 1.464982{col 37}{space 2} .5061962{col 48}{space 1}    1.11{col 57}{space 3}0.269{col 65}{space 4} .7442451{col 78}{space 3} 2.883689
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} .9646375{col 37}{space 2} .5098132{col 48}{space 1}   -0.07{col 57}{space 3}0.946{col 65}{space 4}  .342375{col 78}{space 3} 2.717854
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2} .5973129{col 37}{space 2} .4161779{col 48}{space 1}   -0.74{col 57}{space 3}0.460{col 65}{space 4} .1524499{col 78}{space 3} 2.340328
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} .7253355{col 37}{space 2} .2340453{col 48}{space 1}   -1.00{col 57}{space 3}0.320{col 65}{space 4} .3853721{col 78}{space 3} 1.365204
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2} .8881639{col 37}{space 2} .3128287{col 48}{space 1}   -0.34{col 57}{space 3}0.736{col 65}{space 4} .4453314{col 78}{space 3} 1.771344
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} .5494259{col 37}{space 2} .2515642{col 48}{space 1}   -1.31{col 57}{space 3}0.191{col 65}{space 4} .2239606{col 78}{space 3} 1.347866
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2} 2.514352{col 37}{space 2}  .927178{col 48}{space 1}    2.50{col 57}{space 3}0.012{col 65}{space 4} 1.220511{col 78}{space 3} 5.179768
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2}  .902232{col 37}{space 2}  .027659{col 48}{space 1}   -3.36{col 57}{space 3}0.001{col 65}{space 4} .8496178{col 78}{space 3} .9581044
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2} .7229107{col 37}{space 2} .3358218{col 48}{space 1}   -0.70{col 57}{space 3}0.485{col 65}{space 4} .2908483{col 78}{space 3} 1.796813
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000013{col 37}{space 2} .0000116{col 48}{space 1}    1.16{col 57}{space 3}0.247{col 65}{space 4} .9999907{col 78}{space 3} 1.000036
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} 1.429126{col 37}{space 2} .5506833{col 48}{space 1}    0.93{col 57}{space 3}0.354{col 65}{space 4} .6715498{col 78}{space 3} 3.041326
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2} 1.112478{col 37}{space 2} .5026604{col 48}{space 1}    0.24{col 57}{space 3}0.814{col 65}{space 4} .4588658{col 78}{space 3}   2.6971
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2} .9818591{col 37}{space 2} .0143218{col 48}{space 1}   -1.26{col 57}{space 3}0.209{col 65}{space 4} .9541864{col 78}{space 3} 1.010334
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2} 1.025965{col 37}{space 2}  .014516{col 48}{space 1}    1.81{col 57}{space 3}0.070{col 65}{space 4} .9979055{col 78}{space 3} 1.054814
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2} 1.062358{col 37}{space 2} .1637227{col 48}{space 1}    0.39{col 57}{space 3}0.695{col 65}{space 4} .7853988{col 78}{space 3} 1.436984
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} .8717184{col 37}{space 2} .1451924{col 48}{space 1}   -0.82{col 57}{space 3}0.410{col 65}{space 4} .6289283{col 78}{space 3} 1.208235
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * outreg2 using Table4.doc, eform tstat bdec(2) tdec(2) word ctitle(Public) replace
. 
. * Plot cumulative incidence function with all explanatory variables at the mean
. stcurve, cif
{res}{txt}
{com}. graph export stcurve4_1a.png, replace
{txt}(file stcurve4_1a.png written in PNG format)

{com}. 
. * Plot cumulative incidence function for central gov. insider yes and no and 
. * with all other explanatory variables held at the mean
. stcurve, cif at1(ceocivil=0) at2(ceocivil=1)
{res}{txt}
{com}. graph export stcurve4_1b.png, replace
{txt}(file stcurve4_1b.png written in PNG format)

{com}. 
. * Destination: private and nonprofit sector
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 3)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 3
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}       1318{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         55{txt}  failures in single-failure-per-subject data
{res}       1345{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score if coxsample == 1, /*
> */ compete(civpubnotpub2 == 1, 4)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 3
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-195.12718}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res: -193.7106}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-193.69091}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-193.69083}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-193.69083}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 3{txt}{col 50}No. failed{col 67}={col 69}{res}        43
{txt}Competing events{col 17}: {res}civpubno~2 == 1 4{txt}{col 50}No. competing{col 67}={col 69}{res}        99
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{txt}Wald chi2({res}18{txt}){col 67}={col 70}{res}    47.29
{txt}Log pseudolikelihood = {res}-193.69083{col 50}{txt}Prob > chi2{col 67}={col 73}{res}0.0002

{txt}{ralign 89:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                     _t{col 25}{c |}        SHR{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ceocivil {c |}{col 25}{res}{space 2} .2815315{col 37}{space 2} .0883826{col 48}{space 1}   -4.04{col 57}{space 3}0.000{col 65}{space 4} .1521616{col 78}{space 3} .5208935
{txt}{space 7}L_high_tarmil_FY {c |}{col 25}{res}{space 2} 1.747186{col 37}{space 2} .6981992{col 48}{space 1}    1.40{col 57}{space 3}0.163{col 65}{space 4} .7983391{col 78}{space 3} 3.823761
{txt}{space 8}L_low_tarmil_FY {c |}{col 25}{res}{space 2} .6714248{col 37}{space 2} .3713853{col 48}{space 1}   -0.72{col 57}{space 3}0.471{col 65}{space 4} .2270764{col 78}{space 3} 1.985284
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} .6838166{col 37}{space 2} .4143862{col 48}{space 1}   -0.63{col 57}{space 3}0.531{col 65}{space 4} .2085066{col 78}{space 3}  2.24264
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2} 1.784022{col 37}{space 2} .7781683{col 48}{space 1}    1.33{col 57}{space 3}0.184{col 65}{space 4} .7587812{col 78}{space 3} 4.194534
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} 1.445047{col 37}{space 2} .4674989{col 48}{space 1}    1.14{col 57}{space 3}0.255{col 65}{space 4} .7664838{col 78}{space 3} 2.724339
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2}  1.16863{col 37}{space 2} .3918527{col 48}{space 1}    0.46{col 57}{space 3}0.642{col 65}{space 4} .6057048{col 78}{space 3} 2.254722
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} 1.347118{col 37}{space 2} .6513435{col 48}{space 1}    0.62{col 57}{space 3}0.538{col 65}{space 4} .5222067{col 78}{space 3} 3.475115
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2}  1.18912{col 37}{space 2} .4434922{col 48}{space 1}    0.46{col 57}{space 3}0.642{col 65}{space 4} .5724834{col 78}{space 3} 2.469951
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2} 1.058479{col 37}{space 2} .0351212{col 48}{space 1}    1.71{col 57}{space 3}0.087{col 65}{space 4} .9918333{col 78}{space 3} 1.129603
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2} .4023662{col 37}{space 2} .2413207{col 48}{space 1}   -1.52{col 57}{space 3}0.129{col 65}{space 4} .1241967{col 78}{space 3} 1.303566
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000008{col 37}{space 2} .0000176{col 48}{space 1}    0.45{col 57}{space 3}0.656{col 65}{space 4} .9999733{col 78}{space 3} 1.000042
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} 1.233099{col 37}{space 2} .4583166{col 48}{space 1}    0.56{col 57}{space 3}0.573{col 65}{space 4} .5951477{col 78}{space 3} 2.554885
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2} 1.500321{col 37}{space 2} .4956528{col 48}{space 1}    1.23{col 57}{space 3}0.219{col 65}{space 4} .7851952{col 78}{space 3} 2.866758
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2} .9987358{col 37}{space 2} .0088898{col 48}{space 1}   -0.14{col 57}{space 3}0.887{col 65}{space 4} .9814632{col 78}{space 3} 1.016312
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2} 1.008049{col 37}{space 2} .0176856{col 48}{space 1}    0.46{col 57}{space 3}0.648{col 65}{space 4} .9739747{col 78}{space 3} 1.043314
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2} .9457811{col 37}{space 2} .1565571{col 48}{space 1}   -0.34{col 57}{space 3}0.736{col 65}{space 4} .6837376{col 78}{space 3} 1.308253
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} 1.121026{col 37}{space 2} .1638725{col 48}{space 1}    0.78{col 57}{space 3}0.434{col 65}{space 4} .8417566{col 78}{space 3}  1.49295
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * outreg2 using Table4.doc, eform tstat bdec(2) tdec(2) word ctitle(Private) append
. 
. * Plot cumulative incidence function with all explanatory variables at the mean
. stcurve, cif
{res}{txt}
{com}. graph export stcurve4_2a.png, replace
{txt}(file stcurve4_2a.png written in PNG format)

{com}. 
. * Plot cumulative incidence function for central gov. insider yes and no and 
. * with all other explanatory variables held at the mean
. stcurve, cif at1(ceocivil=0) at2(ceocivil=1)
{res}{txt}
{com}. graph export stcurve4_2b.png, replace
{txt}(file stcurve4_2b.png written in PNG format)

{com}. 
. * Destination: retirement
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 4)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 4
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}       1318{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         75{txt}  failures in single-failure-per-subject data
{res}       1345{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score if coxsample == 1, /*
> */ compete(civpubnotpub2 == 1, 3)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 4
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-224.70406}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-220.77318}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-220.73202}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-220.73202}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 4{txt}{col 50}No. failed{col 67}={col 69}{res}        60
{txt}Competing events{col 17}: {res}civpubno~2 == 1 3{txt}{col 50}No. competing{col 67}={col 69}{res}        82
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{txt}Wald chi2({res}18{txt}){col 67}={col 70}{res}   124.11
{txt}Log pseudolikelihood = {res}-220.73202{col 50}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 89:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                     _t{col 25}{c |}        SHR{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ceocivil {c |}{col 25}{res}{space 2}  4.70257{col 37}{space 2} 2.079875{col 48}{space 1}    3.50{col 57}{space 3}0.000{col 65}{space 4} 1.976341{col 78}{space 3} 11.18945
{txt}{space 7}L_high_tarmil_FY {c |}{col 25}{res}{space 2}  1.68144{col 37}{space 2} .5901528{col 48}{space 1}    1.48{col 57}{space 3}0.139{col 65}{space 4} .8451354{col 78}{space 3} 3.345312
{txt}{space 8}L_low_tarmil_FY {c |}{col 25}{res}{space 2} 2.223343{col 37}{space 2} .7210378{col 48}{space 1}    2.46{col 57}{space 3}0.014{col 65}{space 4} 1.177495{col 78}{space 3} 4.198113
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} .3411822{col 37}{space 2} .1939449{col 48}{space 1}   -1.89{col 57}{space 3}0.059{col 65}{space 4}  .111975{col 78}{space 3} 1.039565
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2} .4894485{col 37}{space 2} .2925906{col 48}{space 1}   -1.20{col 57}{space 3}0.232{col 65}{space 4} .1516567{col 78}{space 3} 1.579619
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} 1.142746{col 37}{space 2} .2883475{col 48}{space 1}    0.53{col 57}{space 3}0.597{col 65}{space 4} .6968948{col 78}{space 3} 1.873839
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2} .9901367{col 37}{space 2} .3067464{col 48}{space 1}   -0.03{col 57}{space 3}0.974{col 65}{space 4} .5394997{col 78}{space 3} 1.817185
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} .8612328{col 37}{space 2} .2361624{col 48}{space 1}   -0.54{col 57}{space 3}0.586{col 65}{space 4} .5031632{col 78}{space 3} 1.474118
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2} 1.004217{col 37}{space 2} .4717291{col 48}{space 1}    0.01{col 57}{space 3}0.993{col 65}{space 4} .3999237{col 78}{space 3}  2.52161
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2} 1.390003{col 37}{space 2} .0542232{col 48}{space 1}    8.44{col 57}{space 3}0.000{col 65}{space 4} 1.287688{col 78}{space 3} 1.500446
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2} 1.711563{col 37}{space 2} .9164269{col 48}{space 1}    1.00{col 57}{space 3}0.316{col 65}{space 4} .5992826{col 78}{space 3} 4.888257
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000016{col 37}{space 2} .0000234{col 48}{space 1}    0.67{col 57}{space 3}0.504{col 65}{space 4} .9999697{col 78}{space 3} 1.000062
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} .9054715{col 37}{space 2} .3011824{col 48}{space 1}   -0.30{col 57}{space 3}0.765{col 65}{space 4} .4717848{col 78}{space 3} 1.737823
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2} .3181698{col 37}{space 2} .1584524{col 48}{space 1}   -2.30{col 57}{space 3}0.021{col 65}{space 4}  .119881{col 78}{space 3} .8444377
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2} .9831334{col 37}{space 2} .0130774{col 48}{space 1}   -1.28{col 57}{space 3}0.201{col 65}{space 4} .9578334{col 78}{space 3} 1.009102
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2}  .934939{col 37}{space 2} .0272099{col 48}{space 1}   -2.31{col 57}{space 3}0.021{col 65}{space 4} .8831011{col 78}{space 3} .9898197
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2}  .784898{col 37}{space 2} .1380463{col 48}{space 1}   -1.38{col 57}{space 3}0.168{col 65}{space 4} .5560393{col 78}{space 3} 1.107952
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} 1.163062{col 37}{space 2} .1586575{col 48}{space 1}    1.11{col 57}{space 3}0.268{col 65}{space 4} .8901997{col 78}{space 3} 1.519562
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * outreg2 using Table4.doc, eform tstat bdec(2) tdec(2) word ctitle(Retire) append
. 
. * Plot cumulative incidence function with all explanatory variables at the mean
. stcurve, cif
{res}{txt}
{com}. graph export stcurve4_3a.png, replace
{txt}(file stcurve4_3a.png written in PNG format)

{com}. 
. * Plot cumulative incidence function for low performance yes and no and 
. * with all other explanatory variables held at the mean
. stcurve, cif at1(ceocivil=0) at2(ceocivil=1)
{res}{txt}
{com}. graph export stcurve4_3b.png, replace
{txt}(file stcurve4_3b.png written in PNG format)

{com}. 
. * Plot cumulative incidence function for central gov. insider yes and no and 
. * with all other explanatory variables held at the mean
. stcurve, cif at1(L_low_tarmil_FY=0) at2(L_low_tarmil_FY=1)
{res}{txt}
{com}. graph export stcurve4_3c.png, replace
{txt}(file stcurve4_3c.png written in PNG format)

{com}. 
. 
. * Endnote 11: "As a robustness check, we estimated both 
. * the pooled and the competing risks models with a linear 
. * target achievement percentage variable instead of the 
. * dummies for high and low target achievement. In the pooled 
. * model (variation of table 3 ), the linear target achievement 
. * rate is not statistically associated with length of tenure. 
. * In the competing risks model (variation of table 4 ), it is 
. * positively associated with length of tenure for chief 
. * executives who exited to another central government 
. * position: a one percentage point increase in the target 
. * achievement rate is associated with a 2 percent lower risk 
. * of exit into another position in the public sector."
. *****
. 
. * (i) all departures, without disaggregating by type of destination
. * Specification with linear target achievement percentage
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 1, 3, 4)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 1 3 4
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}         21{txt}  observations begin on or after (first) failure
{hline 78}
{res}       1297{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}        201{txt}  failures in single-failure-per-subject data
{res}       1324{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcox /*
> */ ceocivil /*
> */ L_targsmilespct_met /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score if coxsample == 1

         {txt}failure _d:  {res}civpubnotpub2 == 1 3 4
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:   log likelihood = {res}-633.81802
{txt}Iteration 1:   log likelihood = {res}-614.58446
{txt}Iteration 2:   log likelihood = {res}-613.78587
{txt}Iteration 3:   log likelihood = {res}-613.77292
{txt}Iteration 4:   log likelihood = {res} -613.7729
{txt}Refining estimates:
Iteration 0:   log likelihood = {res} -613.7729

{txt}Cox regression -- Breslow method for ties

No. of subjects = {res}         247                  {txt}Number of obs    =  {res}       934
{txt}No. of failures = {res}         142
{txt}Time at risk    = {res}         934
                                                {txt}LR chi2({res}17{txt})      =  {res}     40.09
{txt}Log likelihood  =   {res} -613.7729                  {txt}Prob > chi2      =  {res}    0.0013

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                     _t{col 25}{c |} Haz. Ratio{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ceocivil {c |}{col 25}{res}{space 2} 1.262144{col 37}{space 2} .2650865{col 48}{space 1}    1.11{col 57}{space 3}0.268{col 65}{space 4} .8362424{col 78}{space 3} 1.904959
{txt}{space 4}L_targsmilespct_met {c |}{col 25}{res}{space 2} .9954743{col 37}{space 2}  .003987{col 48}{space 1}   -1.13{col 57}{space 3}0.257{col 65}{space 4} .9876905{col 78}{space 3} 1.003319
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} .5589797{col 37}{space 2}  .194363{col 48}{space 1}   -1.67{col 57}{space 3}0.094{col 65}{space 4} .2827643{col 78}{space 3} 1.105013
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2} .8616466{col 37}{space 2} .2761889{col 48}{space 1}   -0.46{col 57}{space 3}0.642{col 65}{space 4} .4597146{col 78}{space 3} 1.614991
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} 1.117405{col 37}{space 2} .2063199{col 48}{space 1}    0.60{col 57}{space 3}0.548{col 65}{space 4} .7781132{col 78}{space 3} 1.604642
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2} .9297486{col 37}{space 2} .1778985{col 48}{space 1}   -0.38{col 57}{space 3}0.703{col 65}{space 4} .6389933{col 78}{space 3} 1.352804
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} .9153409{col 37}{space 2}   .20307{col 48}{space 1}   -0.40{col 57}{space 3}0.690{col 65}{space 4} .5925736{col 78}{space 3} 1.413916
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2} 1.811907{col 37}{space 2} .3900983{col 48}{space 1}    2.76{col 57}{space 3}0.006{col 65}{space 4}  1.18816{col 78}{space 3} 2.763104
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2} 1.074037{col 37}{space 2} .0227634{col 48}{space 1}    3.37{col 57}{space 3}0.001{col 65}{space 4} 1.030336{col 78}{space 3} 1.119592
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2}  .732849{col 37}{space 2} .2477124{col 48}{space 1}   -0.92{col 57}{space 3}0.358{col 65}{space 4} .3778309{col 78}{space 3}  1.42145
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000019{col 37}{space 2} 9.08e-06{col 48}{space 1}    2.06{col 57}{space 3}0.039{col 65}{space 4} 1.000001{col 78}{space 3} 1.000037
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} .8368155{col 37}{space 2} .1943374{col 48}{space 1}   -0.77{col 57}{space 3}0.443{col 65}{space 4}  .530825{col 78}{space 3} 1.319192
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2}  .860645{col 37}{space 2} .1988211{col 48}{space 1}   -0.65{col 57}{space 3}0.516{col 65}{space 4} .5472485{col 78}{space 3} 1.353516
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2} .9883774{col 37}{space 2} .0075119{col 48}{space 1}   -1.54{col 57}{space 3}0.124{col 65}{space 4} .9737634{col 78}{space 3} 1.003211
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2} .9859119{col 37}{space 2}  .014261{col 48}{space 1}   -0.98{col 57}{space 3}0.327{col 65}{space 4} .9583534{col 78}{space 3} 1.014263
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2} .8681602{col 37}{space 2} .0924439{col 48}{space 1}   -1.33{col 57}{space 3}0.184{col 65}{space 4} .7046311{col 78}{space 3} 1.069641
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} 1.054872{col 37}{space 2} .1029772{col 48}{space 1}    0.55{col 57}{space 3}0.584{col 65}{space 4} .8711746{col 78}{space 3} 1.277305
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * (ii) sector competing risks
. * Specification with linear target achievement percentage
. 
. * Destination: public sector
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub == 1)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 1
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}         21{txt}  observations begin on or after (first) failure
{hline 78}
{res}       1297{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         71{txt}  failures in single-failure-per-subject data
{res}       1324{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_targsmilespct_met /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score if coxsample == 1, /*
> */ compete(civpubnotpub == 3, 4)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 1
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -182.5649}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-181.90485}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-181.90249}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-181.90249}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 1{txt}{col 50}No. failed{col 67}={col 69}{res}        39
{txt}Competing events{col 17}: {res}civpubno~2 == 3 4{txt}{col 50}No. competing{col 67}={col 69}{res}       103
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{txt}Wald chi2({res}17{txt}){col 67}={col 70}{res}    38.13
{txt}Log pseudolikelihood = {res}-181.90249{col 50}{txt}Prob > chi2{col 67}={col 73}{res}0.0024

{txt}{ralign 89:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                     _t{col 25}{c |}        SHR{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ceocivil {c |}{col 25}{res}{space 2} 2.762668{col 37}{space 2} 1.213483{col 48}{space 1}    2.31{col 57}{space 3}0.021{col 65}{space 4} 1.168004{col 78}{space 3}  6.53451
{txt}{space 4}L_targsmilespct_met {c |}{col 25}{res}{space 2} .9858252{col 37}{space 2}  .005776{col 48}{space 1}   -2.44{col 57}{space 3}0.015{col 65}{space 4} .9745692{col 78}{space 3} .9972112
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} 1.066343{col 37}{space 2} .5672258{col 48}{space 1}    0.12{col 57}{space 3}0.904{col 65}{space 4} .3759348{col 78}{space 3} 3.024693
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2} .6380129{col 37}{space 2} .4360607{col 48}{space 1}   -0.66{col 57}{space 3}0.511{col 65}{space 4} .1671327{col 78}{space 3} 2.435553
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} .7629976{col 37}{space 2} .2475849{col 48}{space 1}   -0.83{col 57}{space 3}0.404{col 65}{space 4} .4039401{col 78}{space 3} 1.441217
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2}  .953143{col 37}{space 2} .3438852{col 48}{space 1}   -0.13{col 57}{space 3}0.894{col 65}{space 4} .4699508{col 78}{space 3} 1.933142
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} .5497475{col 37}{space 2} .2573116{col 48}{space 1}   -1.28{col 57}{space 3}0.201{col 65}{space 4} .2196619{col 78}{space 3} 1.375852
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2}   2.5532{col 37}{space 2} .9430901{col 48}{space 1}    2.54{col 57}{space 3}0.011{col 65}{space 4} 1.237861{col 78}{space 3} 5.266208
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2} .9007249{col 37}{space 2} .0285578{col 48}{space 1}   -3.30{col 57}{space 3}0.001{col 65}{space 4} .8464564{col 78}{space 3} .9584728
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2} .7204309{col 37}{space 2} .3368486{col 48}{space 1}   -0.70{col 57}{space 3}0.483{col 65}{space 4} .2881375{col 78}{space 3} 1.801295
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000015{col 37}{space 2} .0000117{col 48}{space 1}    1.29{col 57}{space 3}0.198{col 65}{space 4} .9999921{col 78}{space 3} 1.000038
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} 1.528879{col 37}{space 2} .5843766{col 48}{space 1}    1.11{col 57}{space 3}0.267{col 65}{space 4} .7228067{col 78}{space 3} 3.233881
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2} 1.128178{col 37}{space 2} .5102666{col 48}{space 1}    0.27{col 57}{space 3}0.790{col 65}{space 4} .4649277{col 78}{space 3} 2.737599
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2}  .981697{col 37}{space 2} .0136159{col 48}{space 1}   -1.33{col 57}{space 3}0.183{col 65}{space 4} .9553697{col 78}{space 3}  1.00875
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2}  1.03383{col 37}{space 2} .0144144{col 48}{space 1}    2.39{col 57}{space 3}0.017{col 65}{space 4} 1.005961{col 78}{space 3} 1.062471
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2} 1.054697{col 37}{space 2} .1624718{col 48}{space 1}    0.35{col 57}{space 3}0.730{col 65}{space 4} .7798361{col 78}{space 3} 1.426434
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} .9023357{col 37}{space 2} .1493634{col 48}{space 1}   -0.62{col 57}{space 3}0.535{col 65}{space 4} .6523324{col 78}{space 3} 1.248152
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * Destination: private and nonprofit sector
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub == 3)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 3
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}       1318{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         55{txt}  failures in single-failure-per-subject data
{res}       1345{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_targsmilespct_met /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score if coxsample == 1, /*
> */ compete(civpubnotpub == 1, 4)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 3
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-195.72798}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-194.23931}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-194.21456}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-194.21449}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-194.21449}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 3{txt}{col 50}No. failed{col 67}={col 69}{res}        43
{txt}Competing events{col 17}: {res}civpubno~2 == 1 4{txt}{col 50}No. competing{col 67}={col 69}{res}        99
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{txt}Wald chi2({res}17{txt}){col 67}={col 70}{res}    43.35
{txt}Log pseudolikelihood = {res}-194.21449{col 50}{txt}Prob > chi2{col 67}={col 73}{res}0.0004

{txt}{ralign 89:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                     _t{col 25}{c |}        SHR{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ceocivil {c |}{col 25}{res}{space 2} .2843672{col 37}{space 2} .0879003{col 48}{space 1}   -4.07{col 57}{space 3}0.000{col 65}{space 4}  .155155{col 78}{space 3} .5211864
{txt}{space 4}L_targsmilespct_met {c |}{col 25}{res}{space 2} 1.013098{col 37}{space 2} .0105823{col 48}{space 1}    1.25{col 57}{space 3}0.213{col 65}{space 4}  .992568{col 78}{space 3} 1.034053
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} .6485638{col 37}{space 2} .3856107{col 48}{space 1}   -0.73{col 57}{space 3}0.466{col 65}{space 4} .2022375{col 78}{space 3} 2.079907
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2} 1.742106{col 37}{space 2} .7429212{col 48}{space 1}    1.30{col 57}{space 3}0.193{col 65}{space 4} .7552304{col 78}{space 3} 4.018551
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} 1.391765{col 37}{space 2} .4463255{col 48}{space 1}    1.03{col 57}{space 3}0.303{col 65}{space 4} .7423244{col 78}{space 3} 2.609384
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2} 1.089629{col 37}{space 2} .3459555{col 48}{space 1}    0.27{col 57}{space 3}0.787{col 65}{space 4} .5848217{col 78}{space 3} 2.030177
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} 1.320428{col 37}{space 2} .6382488{col 48}{space 1}    0.58{col 57}{space 3}0.565{col 65}{space 4} .5120045{col 78}{space 3} 3.405304
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2} 1.189515{col 37}{space 2} .4442002{col 48}{space 1}    0.46{col 57}{space 3}0.642{col 65}{space 4}  .572145{col 78}{space 3} 2.473056
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2} 1.056069{col 37}{space 2} .0341348{col 48}{space 1}    1.69{col 57}{space 3}0.091{col 65}{space 4} .9912413{col 78}{space 3} 1.125137
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2} .3961726{col 37}{space 2} .2438565{col 48}{space 1}   -1.50{col 57}{space 3}0.133{col 65}{space 4} .1185614{col 78}{space 3} 1.323809
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000006{col 37}{space 2} .0000177{col 48}{space 1}    0.34{col 57}{space 3}0.732{col 65}{space 4} .9999714{col 78}{space 3} 1.000041
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} 1.180582{col 37}{space 2} .4329309{col 48}{space 1}    0.45{col 57}{space 3}0.651{col 65}{space 4} .5753763{col 78}{space 3} 2.422367
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2} 1.481455{col 37}{space 2} .4937471{col 48}{space 1}    1.18{col 57}{space 3}0.238{col 65}{space 4} .7708958{col 78}{space 3}  2.84696
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2} .9993024{col 37}{space 2} .0085621{col 48}{space 1}   -0.08{col 57}{space 3}0.935{col 65}{space 4} .9826612{col 78}{space 3} 1.016225
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2}  1.00417{col 37}{space 2} .0179458{col 48}{space 1}    0.23{col 57}{space 3}0.816{col 65}{space 4} .9696055{col 78}{space 3} 1.039966
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2}  .942959{col 37}{space 2} .1521039{col 48}{space 1}   -0.36{col 57}{space 3}0.716{col 65}{space 4} .6873689{col 78}{space 3} 1.293587
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} 1.131449{col 37}{space 2} .1651284{col 48}{space 1}    0.85{col 57}{space 3}0.397{col 65}{space 4} .8499769{col 78}{space 3} 1.506132
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * Destination: retirement
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub == 4)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 4
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}       1318{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         75{txt}  failures in single-failure-per-subject data
{res}       1345{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_targsmilespct_met /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score if coxsample == 1, /*
> */ compete(civpubnotpub == 1, 3)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 4
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-227.42546}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-223.16599}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:  -223.105}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-223.10499}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 4{txt}{col 50}No. failed{col 67}={col 69}{res}        60
{txt}Competing events{col 17}: {res}civpubno~2 == 1 3{txt}{col 50}No. competing{col 67}={col 69}{res}        82
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{txt}Wald chi2({res}17{txt}){col 67}={col 70}{res}    99.43
{txt}Log pseudolikelihood = {res}-223.10499{col 50}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 89:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                     _t{col 25}{c |}        SHR{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ceocivil {c |}{col 25}{res}{space 2} 4.809102{col 37}{space 2} 2.130789{col 48}{space 1}    3.54{col 57}{space 3}0.000{col 65}{space 4} 2.017988{col 78}{space 3} 11.46065
{txt}{space 4}L_targsmilespct_met {c |}{col 25}{res}{space 2} .9943321{col 37}{space 2} .0060139{col 48}{space 1}   -0.94{col 57}{space 3}0.347{col 65}{space 4} .9826147{col 78}{space 3} 1.006189
{txt}{space 12}L_newparty2 {c |}{col 25}{res}{space 2} .3719646{col 37}{space 2} .2033183{col 48}{space 1}   -1.81{col 57}{space 3}0.070{col 65}{space 4} .1274176{col 78}{space 3}  1.08586
{txt}{space 14}newparty2 {c |}{col 25}{res}{space 2} .4635394{col 37}{space 2}  .266123{col 48}{space 1}   -1.34{col 57}{space 3}0.180{col 65}{space 4} .1504536{col 78}{space 3}  1.42814
{txt}{space 15}L_newMIN {c |}{col 25}{res}{space 2} 1.134666{col 37}{space 2}  .302476{col 48}{space 1}    0.47{col 57}{space 3}0.636{col 65}{space 4} .6729107{col 78}{space 3} 1.913282
{txt}{space 17}newMIN {c |}{col 25}{res}{space 2} 1.030013{col 37}{space 2} .3117253{col 48}{space 1}    0.10{col 57}{space 3}0.922{col 65}{space 4} .5691584{col 78}{space 3} 1.864026
{txt}{space 15}badegree {c |}{col 25}{res}{space 2} .8813546{col 37}{space 2} .2504017{col 48}{space 1}   -0.44{col 57}{space 3}0.657{col 65}{space 4} .5050284{col 78}{space 3} 1.538104
{txt}{space 12}ceooxbridge {c |}{col 25}{res}{space 2} .8226357{col 37}{space 2} .4199785{col 48}{space 1}   -0.38{col 57}{space 3}0.702{col 65}{space 4} .3024443{col 78}{space 3} 2.237534
{txt}{space 17}ceoage {c |}{col 25}{res}{space 2}  1.38008{col 37}{space 2} .0555932{col 48}{space 1}    8.00{col 57}{space 3}0.000{col 65}{space 4}  1.27531{col 78}{space 3} 1.493458
{txt}{space 14}ceofemale {c |}{col 25}{res}{space 2} 1.943057{col 37}{space 2} 1.038456{col 48}{space 1}    1.24{col 57}{space 3}0.214{col 65}{space 4} .6816567{col 78}{space 3} 5.538669
{txt}{space 18}staff {c |}{col 25}{res}{space 2} 1.000009{col 37}{space 2} .0000265{col 48}{space 1}    0.32{col 57}{space 3}0.746{col 65}{space 4} .9999567{col 78}{space 3}  1.00006
{txt}{space 13}regulatory {c |}{col 25}{res}{space 2} .9211956{col 37}{space 2} .3038026{col 48}{space 1}   -0.25{col 57}{space 3}0.803{col 65}{space 4} .4826504{col 78}{space 3} 1.758211
{txt}{space 11}trading_fund {c |}{col 25}{res}{space 2} .3018924{col 37}{space 2} .1474179{col 48}{space 1}   -2.45{col 57}{space 3}0.014{col 65}{space 4}  .115931{col 78}{space 3} .7861487
{txt}L_pc_effoutcome_targets {c |}{col 25}{res}{space 2} .9854108{col 37}{space 2} .0136208{col 48}{space 1}   -1.06{col 57}{space 3}0.288{col 65}{space 4} .9590728{col 78}{space 3} 1.012472
{txt}{space 7}L_targsmiles_set {c |}{col 25}{res}{space 2} .9267384{col 37}{space 2} .0269859{col 48}{space 1}   -2.61{col 57}{space 3}0.009{col 65}{space 4}  .875328{col 78}{space 3} .9811683
{txt}{space 8}L_media_Z_score {c |}{col 25}{res}{space 2}  .768127{col 37}{space 2} .1306884{col 48}{space 1}   -1.55{col 57}{space 3}0.121{col 65}{space 4} .5503138{col 78}{space 3}  1.07215
{txt}{space 10}media_Z_score {c |}{col 25}{res}{space 2} 1.178095{col 37}{space 2} .1593485{col 48}{space 1}    1.21{col 57}{space 3}0.226{col 65}{space 4}  .903748{col 78}{space 3} 1.535725
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. * Endnote 5: "We conducted two empirical checks to examine 
. * whether there are patterns consistent with gaming. First, 
. * we reviewed target churn, that is, the percentage of 
. * targets in a year that are new because that could reflect 
. * the substitution of new, perhaps (although not necessarily) * easier targets. We examined whether the rate of target churn 
. * depends on the number of years of an agency head's tenure. 
. * Regressing the percentage of targets that are new in a year 
. * on the number of years the agency head has served, we find 
. * a weak negative relationship, with an additional year in 
. * their job predicting a one percentage point lower rate of 
. * target churn. This pattern is not consistent with gaming. 
. * Second, we examined whether the number of years an agency 
. * head has served predicts the rate of target achievement, 
. * which would be consistent with agency heads learning how 
. * to game over time to improve target performance. We do 
. * not find evidence of such a relationship."
. *****
. 
. * Does the rate of target churn differ by CEO duration?  
. gen pcttargetsnew = 100* (new_targets / targsmiles_set)
{txt}(271 missing values generated)

{com}. list agencyID agencyname financialyear if pcttargetsnew > 100 & /*
> */ pcttargetsnew ~= .
{txt}
      {c TLC}{hline 10}{c -}{hline 44}{c -}{hline 10}{c TRC}
      {c |} {res}agencyID                                   agencyname   financ~r {txt}{c |}
      {c LT}{hline 10}{c -}{hline 44}{c -}{hline 10}{c RT}
 233. {c |} {res}      45   Defence Postal and Courier Services Agency       1995 {txt}{c |}
 377. {c |} {res}      68                         Fire Service College       2008 {txt}{c |}
1114. {c |} {res}     181         Social Security Child Support Agency       2004 {txt}{c |}
      {c BLC}{hline 10}{c -}{hline 44}{c -}{hline 10}{c BRC}

{com}. replace pcttargetsnew = . if pcttargetsnew > 100
{txt}(3 real changes made, 3 to missing)

{com}. sum pcttargetsnew, d

                        {txt}pcttargetsnew
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,044
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}      1,044

{txt}50%    {res}       20                      {txt}Mean          {res}  27.6481
                        {txt}Largest       Std. Dev.     {res} 28.45164
{txt}75%    {res} 45.45454            100
{txt}90%    {res} 71.42857            100       {txt}Variance      {res} 809.4957
{txt}95%    {res} 85.71429            100       {txt}Skewness      {res} .8644584
{txt}99%    {res}      100            100       {txt}Kurtosis      {res} 2.729765
{txt}
{com}. reg pcttargetsnew ceoduration

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,044
{txt}{hline 13}{c +}{hline 34}   F(1, 1042)      = {res}    13.59
{txt}       Model {c |} {res}  10871.878         1   10871.878   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 833432.179     1,042  799.838943   {txt}R-squared       ={res}    0.0129
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0119
{txt}       Total {c |} {res} 844304.057     1,043   809.49574   {txt}Root MSE        =   {res} 28.281

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}pcttargets~w{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}ceoduration {c |}{col 14}{res}{space 2}-1.324442{col 26}{space 2} .3592378{col 37}{space 1}   -3.69{col 46}{space 3}0.000{col 54}{space 4}-2.029354{col 67}{space 3}-.6195304
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 32.78095{col 26}{space 2} 1.644507{col 37}{space 1}   19.93{col 46}{space 3}0.000{col 54}{space 4} 29.55402{col 67}{space 3} 36.00787
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Does the rate of target achievement differ by CEO duration?  
. reg targsmilespct_met ceoduration

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,263
{txt}{hline 13}{c +}{hline 34}   F(1, 1261)      = {res}     0.85
{txt}       Model {c |} {res} 382.959303         1  382.959303   {txt}Prob > F        ={res}    0.3576
{txt}    Residual {c |} {res} 570209.686     1,261   452.18849   {txt}R-squared       ={res}    0.0007
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0001
{txt}       Total {c |} {res} 570592.645     1,262  452.133633   {txt}Root MSE        =   {res} 21.265

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}targsm~t_met{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}ceoduration {c |}{col 14}{res}{space 2} .2280483{col 26}{space 2} .2478051{col 37}{space 1}    0.92{col 46}{space 3}0.358{col 54}{space 4}-.2581074{col 67}{space 3} .7142041
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 76.46071{col 26}{space 2} 1.053935{col 37}{space 1}   72.55{col 46}{space 3}0.000{col 54}{space 4} 74.39305{col 67}{space 3} 78.52837
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. * Endnote 6: "As a robustness check, we reestimated our 
. * models with the addition of dummies for all parent 
. * departments. First, in the pooled model, only 3 out of 
. * 36 department dummies had a statistically significant 
. * hazard ratio: Department for Innovation, Universities 
. * and Skills; Department for Constitutional Affairs; and 
. * Treasury Solicitor í s Department. We note that this is 
. * not much more of a pattern than would be expected by 
. * chance so it does not appear that there are important 
. * differences between ministries of this kind. Second, in 
. * that model, our findings are largely unchanged (the two 
. * exceptions are that the hazard ratios on party political 
. * change and number of agency employees now become 
. * statistically insignificant using a .1 cutoff ). In the 
. * competing risks model, for exits into the public and 
. * private sectors, the relative risk ratios are unchanged 
. * in statistical significance and close numerically. For 
. * exits into retirement, the only differences are that 
. * the relative risk ratios on party political change, 
. * trading fund status, and number targets now become 
. * statistically insignificant using a .1 cutoff."
. *****
. 
. * Table 3 w/ parent dept. dummies: all departures, without disaggregating by type of dest. 
. * Specification with high and low target achievement dummies
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 1, 3, 4)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 1 3 4
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}         21{txt}  observations begin on or after (first) failure
{hline 78}
{res}       1297{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}        201{txt}  failures in single-failure-per-subject data
{res}       1324{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcox ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score /*
> */ i.dept_this_yr if coxsample == 1

         {txt}failure _d:  {res}civpubnotpub2 == 1 3 4
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:   log likelihood = {res}-633.81802
{txt}Iteration 1:   log likelihood = {res}-606.03301
{txt}Iteration 2:   log likelihood = {res}-593.90449
{txt}Iteration 3:   log likelihood = {res}-592.73361
{txt}Iteration 4:   log likelihood = {res}-592.44914
{txt}Iteration 5:   log likelihood = {res}-592.34837
{txt}Iteration 6:   log likelihood = {res}-592.31135
{txt}Iteration 7:   log likelihood = {res}-592.29774
{txt}Iteration 8:   log likelihood = {res}-592.29274
{txt}Iteration 9:   log likelihood = {res}-592.29089
{txt}Iteration 10:  log likelihood = {res}-592.29022
{txt}Iteration 11:  log likelihood = {res}-592.28997
{txt}Iteration 12:  log likelihood = {res}-592.28988
{txt}Iteration 13:  log likelihood = {res}-592.28984
{txt}Iteration 14:  log likelihood = {res}-592.28983
{txt}Iteration 15:  log likelihood = {res}-592.28983
{txt}Iteration 16:  log likelihood = {res}-592.28982
{txt}Iteration 17:  log likelihood = {res}-592.28982
{txt}Iteration 18:  log likelihood = {res}-592.28982
{txt}Iteration 19:  log likelihood = {res}-592.28982
{txt}Iteration 20:  log likelihood = {res}-592.28982
{txt}Iteration 21:  log likelihood = {res}-592.28982
{txt}Iteration 22:  log likelihood = {res}-592.28982
{txt}Iteration 23:  log likelihood = {res}-592.28982
{txt}Iteration 24:  log likelihood = {res}-592.28982
{txt}Iteration 25:  log likelihood = {res}-592.28982
{txt}Iteration 26:  log likelihood = {res}-592.28982
{txt}Iteration 27:  log likelihood = {res}-592.28982
{txt}Iteration 28:  log likelihood = {res}-592.28982
{txt}Iteration 29:  log likelihood = {res}-592.28982
{txt}Iteration 30:  log likelihood = {res}-592.28982
{txt}Iteration 31:  log likelihood = {res}-592.28982
{txt}Iteration 32:  log likelihood = {res}-592.28982
{txt}Iteration 33:  log likelihood = {res}-592.28982
{txt}Iteration 34:  log likelihood = {res}-592.28982
{txt}Iteration 35:  log likelihood = {res}-592.28982
{txt}Iteration 36:  log likelihood = {res}-592.28982
{txt}Iteration 37:  log likelihood = {res}-592.28982
{txt}Iteration 38:  log likelihood = {res}-592.28982
{txt}Iteration 39:  log likelihood = {res}-592.28982
{txt}Iteration 40:  log likelihood = {res}-592.28982
{txt}Iteration 41:  log likelihood = {res}-592.28982
{txt}Iteration 42:  log likelihood = {res}-592.28982
{txt}Iteration 43:  log likelihood = {res}-592.28982
{txt}Iteration 44:  log likelihood = {res}-592.28982
{txt}Iteration 45:  log likelihood = {res}-592.28982
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-592.28982

{txt}Cox regression -- Breslow method for ties

No. of subjects = {res}         247                  {txt}Number of obs    =  {res}       934
{txt}No. of failures = {res}         142
{txt}Time at risk    = {res}         934
                                                {txt}LR chi2({res}54{txt})      =  {res}     83.06
{txt}Log likelihood  =   {res}-592.28982                  {txt}Prob > chi2      =  {res}    0.0067

{txt}{hline 41}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                                      _t{col 42}{c |} Haz. Ratio{col 54}   Std. Err.{col 66}      z{col 74}   P>|z|{col 82}     [95% Con{col 95}f. Interval]
{hline 41}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 32}ceocivil {c |}{col 42}{res}{space 2} 1.104304{col 54}{space 2} .2765993{col 65}{space 1}    0.40{col 74}{space 3}0.692{col 82}{space 4} .6759035{col 95}{space 3} 1.804232
{txt}{space 24}L_high_tarmil_FY {c |}{col 42}{res}{space 2} 1.065283{col 54}{space 2}   .26385{col 65}{space 1}    0.26{col 74}{space 3}0.798{col 82}{space 4} .6555996{col 95}{space 3} 1.730976
{txt}{space 25}L_low_tarmil_FY {c |}{col 42}{res}{space 2} 1.300351{col 54}{space 2} .3080412{col 65}{space 1}    1.11{col 74}{space 3}0.268{col 82}{space 4} .8173701{col 95}{space 3} 2.068723
{txt}{space 29}L_newparty2 {c |}{col 42}{res}{space 2} .5744118{col 54}{space 2} .2068565{col 65}{space 1}   -1.54{col 74}{space 3}0.124{col 82}{space 4} .2835891{col 95}{space 3} 1.163475
{txt}{space 31}newparty2 {c |}{col 42}{res}{space 2} .8869342{col 54}{space 2} .2988494{col 65}{space 1}   -0.36{col 74}{space 3}0.722{col 82}{space 4} .4582284{col 95}{space 3} 1.716725
{txt}{space 32}L_newMIN {c |}{col 42}{res}{space 2} 1.237961{col 54}{space 2} .2566689{col 65}{space 1}    1.03{col 74}{space 3}0.303{col 82}{space 4} .8245664{col 95}{space 3}  1.85861
{txt}{space 34}newMIN {c |}{col 42}{res}{space 2} 1.039073{col 54}{space 2} .2207736{col 65}{space 1}    0.18{col 74}{space 3}0.857{col 82}{space 4}  .685157{col 95}{space 3} 1.575805
{txt}{space 32}badegree {c |}{col 42}{res}{space 2} .7684402{col 54}{space 2} .1855697{col 65}{space 1}   -1.09{col 74}{space 3}0.275{col 82}{space 4} .4786901{col 95}{space 3} 1.233576
{txt}{space 29}ceooxbridge {c |}{col 42}{res}{space 2} 1.675021{col 54}{space 2} .4163576{col 65}{space 1}    2.08{col 74}{space 3}0.038{col 82}{space 4} 1.029054{col 95}{space 3} 2.726479
{txt}{space 34}ceoage {c |}{col 42}{res}{space 2} 1.077185{col 54}{space 2} .0240778{col 65}{space 1}    3.33{col 74}{space 3}0.001{col 82}{space 4} 1.031012{col 95}{space 3} 1.125426
{txt}{space 31}ceofemale {c |}{col 42}{res}{space 2}  .639273{col 54}{space 2} .2479882{col 65}{space 1}   -1.15{col 74}{space 3}0.249{col 82}{space 4} .2988728{col 95}{space 3} 1.367371
{txt}{space 35}staff {c |}{col 42}{res}{space 2} 1.000015{col 54}{space 2} .0000139{col 65}{space 1}    1.11{col 74}{space 3}0.266{col 82}{space 4} .9999882{col 95}{space 3} 1.000043
{txt}{space 30}regulatory {c |}{col 42}{res}{space 2}  .864384{col 54}{space 2} .3255319{col 65}{space 1}   -0.39{col 74}{space 3}0.699{col 82}{space 4}   .41318{col 95}{space 3} 1.808315
{txt}{space 28}trading_fund {c |}{col 42}{res}{space 2} .8833573{col 54}{space 2}   .23541{col 65}{space 1}   -0.47{col 74}{space 3}0.642{col 82}{space 4} .5239571{col 95}{space 3} 1.489283
{txt}{space 17}L_pc_effoutcome_targets {c |}{col 42}{res}{space 2} .9854402{col 54}{space 2} .0089572{col 65}{space 1}   -1.61{col 74}{space 3}0.107{col 82}{space 4} .9680399{col 95}{space 3} 1.003153
{txt}{space 24}L_targsmiles_set {c |}{col 42}{res}{space 2} .9956398{col 54}{space 2} .0161295{col 65}{space 1}   -0.27{col 74}{space 3}0.787{col 82}{space 4} .9645231{col 95}{space 3}  1.02776
{txt}{space 25}L_media_Z_score {c |}{col 42}{res}{space 2}  .877795{col 54}{space 2} .0985006{col 65}{space 1}   -1.16{col 74}{space 3}0.245{col 82}{space 4}  .704493{col 95}{space 3} 1.093729
{txt}{space 27}media_Z_score {c |}{col 42}{res}{space 2} 1.047731{col 54}{space 2}  .108751{col 65}{space 1}    0.45{col 74}{space 3}0.653{col 82}{space 4} .8548657{col 95}{space 3} 1.284108
{txt}{space 40} {c |}
{space 28}dept_this_yr {c |}
{space 25}cabinet office  {c |}{col 42}{res}{space 2} 1.359319{col 54}{space 2} .9743922{col 65}{space 1}    0.43{col 74}{space 3}0.668{col 82}{space 4} .3335493{col 95}{space 3} 5.539657
{txt}{space 15}culture, media and sport  {c |}{col 42}{res}{space 2} .6646629{col 54}{space 2} .7789321{col 65}{space 1}   -0.35{col 74}{space 3}0.727{col 82}{space 4} .0668438{col 95}{space 3} 6.609095
{txt}{space 26}defence (mod)  {c |}{col 42}{res}{space 2} 2.201234{col 54}{space 2} 1.264976{col 65}{space 1}    1.37{col 74}{space 3}0.170{col 82}{space 4} .7136891{col 95}{space 3} 6.789277
{txt}{space 8}education and employment (dfee)  {c |}{col 42}{res}{space 2} 1.283561{col 54}{space 2} 1.629172{col 65}{space 1}    0.20{col 74}{space 3}0.844{col 82}{space 4} .1066646{col 95}{space 3} 15.44588
{txt}{space 22}environment (doe)  {c |}{col 42}{res}{space 2} 1.300231{col 54}{space 2} 1.495234{col 65}{space 1}    0.23{col 74}{space 3}0.819{col 82}{space 4} .1365084{col 95}{space 3} 12.38459
{txt}environment, transport and regions (..)  {c |}{col 42}{res}{space 2} .9702356{col 54}{space 2} .7802433{col 65}{space 1}   -0.04{col 74}{space 3}0.970{col 82}{space 4} .2006128{col 95}{space 3} 4.692409
{txt}{space 2}foreign and commonwealth office (fco)  {c |}{col 42}{res}{space 2} 2.906771{col 54}{space 2} 2.350344{col 65}{space 1}    1.32{col 74}{space 3}0.187{col 82}{space 4} .5958681{col 95}{space 3} 14.17985
{txt}{space 23}health (doh/ dh)  {c |}{col 42}{res}{space 2} .4155831{col 54}{space 2} .3806142{col 65}{space 1}   -0.96{col 74}{space 3}0.338{col 82}{space 4} .0690367{col 95}{space 3} 2.501703
{txt}{space 28}hm treasury  {c |}{col 42}{res}{space 2} 2.143726{col 54}{space 2}  1.43755{col 65}{space 1}    1.14{col 74}{space 3}0.255{col 82}{space 4}  .575926{col 95}{space 3} 7.979433
{txt}{space 28}home office  {c |}{col 42}{res}{space 2} 3.158571{col 54}{space 2} 2.129048{col 65}{space 1}    1.71{col 74}{space 3}0.088{col 82}{space 4}   .84282{col 95}{space 3} 11.83713
{txt}{space 11}lord chancellor's department  {c |}{col 42}{res}{space 2} 2.679393{col 54}{space 2} 2.192533{col 65}{space 1}    1.20{col 74}{space 3}0.228{col 82}{space 4} .5388933{col 95}{space 3} 13.32202
{txt}{space 18}social security (dss)  {c |}{col 42}{res}{space 2} 1.852475{col 54}{space 2} 1.848042{col 65}{space 1}    0.62{col 74}{space 3}0.537{col 82}{space 4}  .262173{col 95}{space 3} 13.08931
{txt}{space 15}trade and industry (dti)  {c |}{col 42}{res}{space 2} 1.893064{col 54}{space 2} 1.238218{col 65}{space 1}    0.98{col 74}{space 3}0.329{col 82}{space 4}  .525303{col 95}{space 3}  6.82214
{txt}{space 27}welsh office  {c |}{col 42}{res}{space 2} 3.435943{col 54}{space 2}  4.04291{col 65}{space 1}    1.05{col 74}{space 3}0.294{col 82}{space 4} .3423559{col 95}{space 3} 34.48373
{txt}environment, food & rural affairs (d..)  {c |}{col 42}{res}{space 2} 1.087192{col 54}{space 2}  .799337{col 65}{space 1}    0.11{col 74}{space 3}0.909{col 82}{space 4} .2573218{col 95}{space 3} 4.593416
{txt}berr (business, enterprise & regulat..)  {c |}{col 42}{res}{space 2} 2.863077{col 54}{space 2}  3.42874{col 65}{space 1}    0.88{col 74}{space 3}0.380{col 82}{space 4} .2738156{col 95}{space 3} 29.93697
{txt}{space 2}bis (business, innovation and skills)  {c |}{col 42}{res}{space 2} 8.35e-20{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}dius (department for innovation, uni..)  {c |}{col 42}{res}{space 2} 6.241261{col 54}{space 2} 6.082047{col 65}{space 1}    1.88{col 74}{space 3}0.060{col 82}{space 4} .9242404{col 95}{space 3} 42.14633
{txt}{space 2}department for constitutional affairs  {c |}{col 42}{res}{space 2} 9.338596{col 54}{space 2} 7.848324{col 65}{space 1}    2.66{col 74}{space 3}0.008{col 82}{space 4} 1.798525{col 95}{space 3} 48.48939
{txt}{space 20}ministry of justice  {c |}{col 42}{res}{space 2}  2.85565{col 54}{space 2} 2.385141{col 65}{space 1}    1.26{col 74}{space 3}0.209{col 82}{space 4} .5555857{col 95}{space 3} 14.67774
{txt}{space 24}transport (dtp)  {c |}{col 42}{res}{space 2} 2.044129{col 54}{space 2}  1.35046{col 65}{space 1}    1.08{col 74}{space 3}0.279{col 82}{space 4} .5599634{col 95}{space 3}  7.46203
{txt}transport, local government & the re..)  {c |}{col 42}{res}{space 2} 1.124778{col 54}{space 2} 1.343881{col 65}{space 1}    0.10{col 74}{space 3}0.922{col 82}{space 4} .1081567{col 95}{space 3} 11.69714
{txt}office of the deputy prime minister ..)  {c |}{col 42}{res}{space 2} 2.30e-19{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}department for communities & local g..)  {c |}{col 42}{res}{space 2} .4659044{col 54}{space 2} .5540768{col 65}{space 1}   -0.64{col 74}{space 3}0.521{col 82}{space 4} .0452903{col 95}{space 3} 4.792788
{txt}scottish executive, rural affairs dep..  {c |}{col 42}{res}{space 2} 2.162406{col 54}{space 2}  2.48542{col 65}{space 1}    0.67{col 74}{space 3}0.502{col 82}{space 4} .2272926{col 95}{space 3} 20.57259
{txt}{space 23}attorney general  {c |}{col 42}{res}{space 2} 1.613322{col 54}{space 2}  1.45895{col 65}{space 1}    0.53{col 74}{space 3}0.597{col 82}{space 4} .2741369{col 95}{space 3} 9.494558
{txt}{space 16}work and pensions (dwp)  {c |}{col 42}{res}{space 2} .7651012{col 54}{space 2} .8880346{col 65}{space 1}   -0.23{col 74}{space 3}0.818{col 82}{space 4} .0786592{col 95}{space 3} 7.441973
{txt}scottish executive, development depar..  {c |}{col 42}{res}{space 2} 3.09e-19{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}{space 13}employment (department of)  {c |}{col 42}{res}{space 2} 1.93e-19{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}{space 7}department for national heritage  {c |}{col 42}{res}{space 2} 2.38e-19{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}scottish executive, education departm..  {c |}{col 42}{res}{space 2} .6873458{col 54}{space 2} .8022689{col 65}{space 1}   -0.32{col 74}{space 3}0.748{col 82}{space 4} .0697677{col 95}{space 3}  6.77168
{txt}{space 24}scottish office  {c |}{col 42}{res}{space 2} 1.568675{col 54}{space 2} 1.063114{col 65}{space 1}    0.66{col 74}{space 3}0.506{col 82}{space 4} .4155863{col 95}{space 3} 5.921133
{txt}Scottish Government, Europe, External..  {c |}{col 42}{res}{space 2} 2.09e-19{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}Scottish Government, Culture, Externa..  {c |}{col 42}{res}{space 2} 2.669988{col 54}{space 2} 3.134305{col 65}{space 1}    0.84{col 74}{space 3}0.403{col 82}{space 4}  .267474{col 95}{space 3} 26.65243
{txt}{space 1}scottish executive, justice department  {c |}{col 42}{res}{space 2} 1.549846{col 54}{space 2} 1.163117{col 65}{space 1}    0.58{col 74}{space 3}0.559{col 82}{space 4} .3560285{col 95}{space 3} 6.746712
{txt}{space 25}inland revenue  {c |}{col 42}{res}{space 2}  2.41018{col 54}{space 2} 1.967589{col 65}{space 1}    1.08{col 74}{space 3}0.281{col 82}{space 4} .4865839{col 95}{space 3} 11.93827
{txt}hmrc (her majesty's revenue and cust..)  {c |}{col 42}{res}{space 2} 3.16e-19{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}{space 8}treasury solicitor's department  {c |}{col 42}{res}{space 2} 9.262118{col 54}{space 2} 11.10613{col 65}{space 1}    1.86{col 74}{space 3}0.063{col 82}{space 4} .8831637{col 95}{space 3} 97.13583
{txt}scottish government, directorates of ..  {c |}{col 42}{res}{space 2} 1.477548{col 54}{space 2}   1.4194{col 65}{space 1}    0.41{col 74}{space 3}0.684{col 82}{space 4}  .224822{col 95}{space 3} 9.710564
{txt}scottish executive, enterprise, trans..  {c |}{col 42}{res}{space 2} 3.91e-20{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}scottish executive, enterprise and li..  {c |}{col 42}{res}{space 2} 1.45e-19{col 54}{space 2}        .{col 65}{space 1}       .{col 74}{space 3}    .{col 82}{space 4}        .{col 95}{space 3}        .
{txt}Scottish Government Directorates of t..  {c |}{col 42}{res}{space 2} 1.793852{col 54}{space 2} 1.633955{col 65}{space 1}    0.64{col 74}{space 3}0.521{col 82}{space 4} .3009249{col 95}{space 3} 10.69339
{txt}Scottish Government, Directorates of ..  {c |}{col 42}{res}{space 2} 3.374149{col 54}{space 2} 3.089813{col 65}{space 1}    1.33{col 74}{space 3}0.184{col 82}{space 4} .5606507{col 95}{space 3} 20.30654
{txt}Scottish Executive, Environment and R..  {c |}{col 42}{res}{space 2} 1.402119{col 54}{space 2} 1.703905{col 65}{space 1}    0.28{col 74}{space 3}0.781{col 82}{space 4} .1295312{col 95}{space 3} 15.17733
{txt}Scottish Executive, Finance & Central..  {c |}{col 42}{res}{space 2} 2.331371{col 54}{space 2} 2.684853{col 65}{space 1}    0.74{col 74}{space 3}0.462{col 82}{space 4}  .243978{col 95}{space 3} 22.27778
{txt}{hline 41}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * Table  w/ parent dept. dummies: sector competing risks
. * Specification with high and low target achievement dummies
. 
. * Destination: public sector
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 1)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 1
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}         21{txt}  observations begin on or after (first) failure
{hline 78}
{res}       1297{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         71{txt}  failures in single-failure-per-subject data
{res}       1324{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score  /*
> */ i.dept_this_yr if coxsample == 1, /*
> */ compete(civpubnotpub2 == 3, 4)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 1
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-161.20335}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res: -159.5501}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-159.49986}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-159.49971}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res: -159.4997}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 1{txt}{col 50}No. failed{col 67}={col 69}{res}        39
{txt}Competing events{col 17}: {res}civpubno~2 == 3 4{txt}{col 50}No. competing{col 67}={col 69}{res}       103
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{help j_robustsingular:Wald chi2(59)}{col 67}{txt}={col 70}{res}        .
{txt}Log pseudolikelihood = {res} -159.4997{col 50}{txt}Prob > chi2{col 67}={col 73}{res}     .

{txt}{ralign 106:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 41}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 42}{c |}{col 54}    Robust
{col 1}                                      _t{col 42}{c |}        SHR{col 54}   Std. Err.{col 66}      z{col 74}   P>|z|{col 82}     [95% Con{col 95}f. Interval]
{hline 41}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 32}ceocivil {c |}{col 42}{res}{space 2} 3.111343{col 54}{space 2} 1.619337{col 65}{space 1}    2.18{col 74}{space 3}0.029{col 82}{space 4} 1.121836{col 95}{space 3} 8.629116
{txt}{space 24}L_high_tarmil_FY {c |}{col 42}{res}{space 2}  .240935{col 54}{space 2} .1812988{col 65}{space 1}   -1.89{col 74}{space 3}0.059{col 82}{space 4}   .05513{col 95}{space 3} 1.052961
{txt}{space 25}L_low_tarmil_FY {c |}{col 42}{res}{space 2} 1.488073{col 54}{space 2}  .579651{col 65}{space 1}    1.02{col 74}{space 3}0.308{col 82}{space 4} .6935132{col 95}{space 3} 3.192963
{txt}{space 29}L_newparty2 {c |}{col 42}{res}{space 2} 1.139747{col 54}{space 2} .6757006{col 65}{space 1}    0.22{col 74}{space 3}0.825{col 82}{space 4} .3565929{col 95}{space 3} 3.642877
{txt}{space 31}newparty2 {c |}{col 42}{res}{space 2} .6846619{col 54}{space 2} .5098446{col 65}{space 1}   -0.51{col 74}{space 3}0.611{col 82}{space 4} .1590798{col 95}{space 3} 2.946709
{txt}{space 32}L_newMIN {c |}{col 42}{res}{space 2} .6215832{col 54}{space 2} .2141051{col 65}{space 1}   -1.38{col 74}{space 3}0.167{col 82}{space 4} .3164476{col 95}{space 3} 1.220947
{txt}{space 34}newMIN {c |}{col 42}{res}{space 2} 1.027965{col 54}{space 2} .3479641{col 65}{space 1}    0.08{col 74}{space 3}0.935{col 82}{space 4} .5294783{col 95}{space 3}  1.99576
{txt}{space 32}badegree {c |}{col 42}{res}{space 2} .5137178{col 54}{space 2} .2626237{col 65}{space 1}   -1.30{col 74}{space 3}0.193{col 82}{space 4} .1886131{col 95}{space 3} 1.399192
{txt}{space 29}ceooxbridge {c |}{col 42}{res}{space 2} 2.980995{col 54}{space 2} 1.330738{col 65}{space 1}    2.45{col 74}{space 3}0.014{col 82}{space 4} 1.242735{col 95}{space 3} 7.150623
{txt}{space 34}ceoage {c |}{col 42}{res}{space 2}  .891074{col 54}{space 2} .0322084{col 65}{space 1}   -3.19{col 74}{space 3}0.001{col 82}{space 4} .8301309{col 95}{space 3} .9564912
{txt}{space 31}ceofemale {c |}{col 42}{res}{space 2} .4625262{col 54}{space 2} .2569082{col 65}{space 1}   -1.39{col 74}{space 3}0.165{col 82}{space 4} .1557184{col 95}{space 3} 1.373829
{txt}{space 35}staff {c |}{col 42}{res}{space 2} .9999914{col 54}{space 2} .0000162{col 65}{space 1}   -0.53{col 74}{space 3}0.594{col 82}{space 4} .9999596{col 95}{space 3} 1.000023
{txt}{space 30}regulatory {c |}{col 42}{res}{space 2} 1.211311{col 54}{space 2} 1.051812{col 65}{space 1}    0.22{col 74}{space 3}0.825{col 82}{space 4} .2208696{col 95}{space 3} 6.643173
{txt}{space 28}trading_fund {c |}{col 42}{res}{space 2} 1.377173{col 54}{space 2} .6534906{col 65}{space 1}    0.67{col 74}{space 3}0.500{col 82}{space 4} .5433502{col 95}{space 3} 3.490577
{txt}{space 17}L_pc_effoutcome_targets {c |}{col 42}{res}{space 2} .9657779{col 54}{space 2} .0182471{col 65}{space 1}   -1.84{col 74}{space 3}0.065{col 82}{space 4} .9306684{col 95}{space 3} 1.002212
{txt}{space 24}L_targsmiles_set {c |}{col 42}{res}{space 2} 1.025717{col 54}{space 2} .0276033{col 65}{space 1}    0.94{col 74}{space 3}0.345{col 82}{space 4} .9730175{col 95}{space 3}  1.08127
{txt}{space 25}L_media_Z_score {c |}{col 42}{res}{space 2} 1.006492{col 54}{space 2} .1638379{col 65}{space 1}    0.04{col 74}{space 3}0.968{col 82}{space 4} .7315614{col 95}{space 3} 1.384745
{txt}{space 27}media_Z_score {c |}{col 42}{res}{space 2} .9693973{col 54}{space 2} .1731763{col 65}{space 1}   -0.17{col 74}{space 3}0.862{col 82}{space 4}  .683031{col 95}{space 3} 1.375825
{txt}{space 40} {c |}
{space 28}dept_this_yr {c |}
{space 25}cabinet office  {c |}{col 42}{res}{space 2}  2356391{col 54}{space 2}  3687174{col 65}{space 1}    9.38{col 74}{space 3}0.000{col 82}{space 4} 109730.1{col 95}{space 3} 5.06e+07
{txt}{space 15}culture, media and sport  {c |}{col 42}{res}{space 2} 1.291553{col 54}{space 2} 1.444779{col 65}{space 1}    0.23{col 74}{space 3}0.819{col 82}{space 4} .1441874{col 95}{space 3} 11.56904
{txt}{space 26}defence (mod)  {c |}{col 42}{res}{space 2}  6066899{col 54}{space 2}  4699045{col 65}{space 1}   20.16{col 74}{space 3}0.000{col 82}{space 4}  1329471{col 95}{space 3} 2.77e+07
{txt}{space 8}education and employment (dfee)  {c |}{col 42}{res}{space 2} 2.38e+08{col 54}{space 2} 2.60e+08{col 65}{space 1}   17.68{col 74}{space 3}0.000{col 82}{space 4} 2.81e+07{col 95}{space 3} 2.02e+09
{txt}{space 22}environment (doe)  {c |}{col 42}{res}{space 2} 1.677645{col 54}{space 2} 1.863689{col 65}{space 1}    0.47{col 74}{space 3}0.641{col 82}{space 4} .1901532{col 95}{space 3} 14.80118
{txt}environment, transport and regions (..)  {c |}{col 42}{res}{space 2} 2.031241{col 54}{space 2} 1.851396{col 65}{space 1}    0.78{col 74}{space 3}0.437{col 82}{space 4} .3403491{col 95}{space 3} 12.12267
{txt}{space 2}foreign and commonwealth office (fco)  {c |}{col 42}{res}{space 2} 1.37e+07{col 54}{space 2} 1.33e+07{col 65}{space 1}   16.87{col 74}{space 3}0.000{col 82}{space 4}  2024302{col 95}{space 3} 9.21e+07
{txt}{space 23}health (doh/ dh)  {c |}{col 42}{res}{space 2} .9261436{col 54}{space 2} .7679156{col 65}{space 1}   -0.09{col 74}{space 3}0.926{col 82}{space 4} .1823481{col 95}{space 3} 4.703871
{txt}{space 28}hm treasury  {c |}{col 42}{res}{space 2}  4329794{col 54}{space 2}  4605877{col 65}{space 1}   14.37{col 74}{space 3}0.000{col 82}{space 4} 538257.5{col 95}{space 3} 3.48e+07
{txt}{space 28}home office  {c |}{col 42}{res}{space 2} 1.32e+07{col 54}{space 2} 1.21e+07{col 65}{space 1}   17.85{col 74}{space 3}0.000{col 82}{space 4}  2180139{col 95}{space 3} 7.99e+07
{txt}{space 11}lord chancellor's department  {c |}{col 42}{res}{space 2}  5658161{col 54}{space 2}  7891525{col 65}{space 1}   11.15{col 74}{space 3}0.000{col 82}{space 4}   367698{col 95}{space 3} 8.71e+07
{txt}{space 18}social security (dss)  {c |}{col 42}{res}{space 2} 1.42e+07{col 54}{space 2} 1.45e+07{col 65}{space 1}   16.18{col 74}{space 3}0.000{col 82}{space 4}  1938617{col 95}{space 3} 1.05e+08
{txt}{space 15}trade and industry (dti)  {c |}{col 42}{res}{space 2}  6881286{col 54}{space 2}  5858295{col 65}{space 1}   18.49{col 74}{space 3}0.000{col 82}{space 4}  1297209{col 95}{space 3} 3.65e+07
{txt}{space 27}welsh office  {c |}{col 42}{res}{space 2} 1.433747{col 54}{space 2} 1.855823{col 65}{space 1}    0.28{col 74}{space 3}0.781{col 82}{space 4} .1134197{col 95}{space 3} 18.12411
{txt}environment, food & rural affairs (d..)  {c |}{col 42}{res}{space 2}  3730558{col 54}{space 2}  3662562{col 65}{space 1}   15.41{col 74}{space 3}0.000{col 82}{space 4} 544611.5{col 95}{space 3} 2.56e+07
{txt}berr (business, enterprise & regulat..)  {c |}{col 42}{res}{space 2} .7168952{col 54}{space 2} .8478735{col 65}{space 1}   -0.28{col 74}{space 3}0.778{col 82}{space 4} .0705891{col 95}{space 3} 7.280708
{txt}{space 2}bis (business, innovation and skills)  {c |}{col 42}{res}{space 2} .6659293{col 54}{space 2} .6888721{col 65}{space 1}   -0.39{col 74}{space 3}0.694{col 82}{space 4}   .08768{col 95}{space 3} 5.057729
{txt}dius (department for innovation, uni..)  {c |}{col 42}{res}{space 2} 5.49e+07{col 54}{space 2} 5.84e+07{col 65}{space 1}   16.76{col 74}{space 3}0.000{col 82}{space 4}  6831745{col 95}{space 3} 4.42e+08
{txt}{space 2}department for constitutional affairs  {c |}{col 42}{res}{space 2} 4.86e+07{col 54}{space 2} 4.46e+07{col 65}{space 1}   19.30{col 74}{space 3}0.000{col 82}{space 4}  8062149{col 95}{space 3} 2.93e+08
{txt}{space 20}ministry of justice  {c |}{col 42}{res}{space 2}  2020967{col 54}{space 2}  2557929{col 65}{space 1}   11.47{col 74}{space 3}0.000{col 82}{space 4} 169120.9{col 95}{space 3} 2.42e+07
{txt}{space 24}transport (dtp)  {c |}{col 42}{res}{space 2}  2175065{col 54}{space 2}  2297435{col 65}{space 1}   13.82{col 74}{space 3}0.000{col 82}{space 4} 274398.4{col 95}{space 3} 1.72e+07
{txt}transport, local government & the re..)  {c |}{col 42}{res}{space 2}   1.8318{col 54}{space 2} 1.714796{col 65}{space 1}    0.65{col 74}{space 3}0.518{col 82}{space 4} .2924461{col 95}{space 3} 11.47388
{txt}office of the deputy prime minister ..)  {c |}{col 42}{res}{space 2} 4.897712{col 54}{space 2}  5.22577{col 65}{space 1}    1.49{col 74}{space 3}0.136{col 82}{space 4} .6050294{col 95}{space 3} 39.64696
{txt}department for communities & local g..)  {c |}{col 42}{res}{space 2} 3.227773{col 54}{space 2} 3.135337{col 65}{space 1}    1.21{col 74}{space 3}0.228{col 82}{space 4} .4809255{col 95}{space 3} 21.66347
{txt}scottish executive, rural affairs dep..  {c |}{col 42}{res}{space 2} 1.821304{col 54}{space 2} 2.215798{col 65}{space 1}    0.49{col 74}{space 3}0.622{col 82}{space 4} .1678074{col 95}{space 3} 19.76761
{txt}{space 23}attorney general  {c |}{col 42}{res}{space 2}  9703425{col 54}{space 2} 1.09e+07{col 65}{space 1}   14.27{col 74}{space 3}0.000{col 82}{space 4}  1064323{col 95}{space 3} 8.85e+07
{txt}{space 16}work and pensions (dwp)  {c |}{col 42}{res}{space 2} 1.084034{col 54}{space 2} 1.022963{col 65}{space 1}    0.09{col 74}{space 3}0.932{col 82}{space 4} .1705277{col 95}{space 3} 6.891132
{txt}scottish executive, development depar..  {c |}{col 42}{res}{space 2} 1.866741{col 54}{space 2} 2.129614{col 65}{space 1}    0.55{col 74}{space 3}0.584{col 82}{space 4} .1995341{col 95}{space 3}  17.4643
{txt}{space 13}employment (department of)  {c |}{col 42}{res}{space 2} 3.463256{col 54}{space 2}  5.12522{col 65}{space 1}    0.84{col 74}{space 3}0.401{col 82}{space 4} .1904602{col 95}{space 3} 62.97453
{txt}{space 7}department for national heritage  {c |}{col 42}{res}{space 2} 18.21493{col 54}{space 2} 25.68383{col 65}{space 1}    2.06{col 74}{space 3}0.040{col 82}{space 4} 1.148675{col 95}{space 3} 288.8405
{txt}scottish executive, education departm..  {c |}{col 42}{res}{space 2} .9968062{col 54}{space 2} 1.092416{col 65}{space 1}   -0.00{col 74}{space 3}0.998{col 82}{space 4} .1163498{col 95}{space 3} 8.539961
{txt}{space 24}scottish office  {c |}{col 42}{res}{space 2}  6699447{col 54}{space 2}  5242690{col 65}{space 1}   20.08{col 74}{space 3}0.000{col 82}{space 4}  1445194{col 95}{space 3} 3.11e+07
{txt}Scottish Government, Europe, External..  {c |}{col 42}{res}{space 2} .6378127{col 54}{space 2} .8504914{col 65}{space 1}   -0.34{col 74}{space 3}0.736{col 82}{space 4} .0467367{col 95}{space 3} 8.704182
{txt}Scottish Government, Culture, Externa..  {c |}{col 42}{res}{space 2}  1.87822{col 54}{space 2} 2.576402{col 65}{space 1}    0.46{col 74}{space 3}0.646{col 82}{space 4} .1276826{col 95}{space 3} 27.62875
{txt}{space 1}scottish executive, justice department  {c |}{col 42}{res}{space 2}  3657559{col 54}{space 2}  3558983{col 65}{space 1}   15.53{col 74}{space 3}0.000{col 82}{space 4} 543163.6{col 95}{space 3} 2.46e+07
{txt}{space 25}inland revenue  {c |}{col 42}{res}{space 2} .5559021{col 54}{space 2} .5379409{col 65}{space 1}   -0.61{col 74}{space 3}0.544{col 82}{space 4} .0834255{col 95}{space 3} 3.704227
{txt}hmrc (her majesty's revenue and cust..)  {c |}{col 42}{res}{space 2} 1.219277{col 54}{space 2} 1.413741{col 65}{space 1}    0.17{col 74}{space 3}0.864{col 82}{space 4}  .125644{col 95}{space 3} 11.83214
{txt}{space 8}treasury solicitor's department  {c |}{col 42}{res}{space 2}  12.0798{col 54}{space 2} 17.46053{col 65}{space 1}    1.72{col 74}{space 3}0.085{col 82}{space 4} .7107315{col 95}{space 3} 205.3118
{txt}scottish government, directorates of ..  {c |}{col 42}{res}{space 2} 1.56e+07{col 54}{space 2} 2.93e+07{col 65}{space 1}    8.81{col 74}{space 3}0.000{col 82}{space 4} 391713.5{col 95}{space 3} 6.20e+08
{txt}scottish executive, enterprise, trans..  {c |}{col 42}{res}{space 2} .4548126{col 54}{space 2} .4335218{col 65}{space 1}   -0.83{col 74}{space 3}0.408{col 82}{space 4} .0702227{col 95}{space 3} 2.945693
{txt}scottish executive, enterprise and li..  {c |}{col 42}{res}{space 2} .7925386{col 54}{space 2} 1.012367{col 65}{space 1}   -0.18{col 74}{space 3}0.856{col 82}{space 4} .0648215{col 95}{space 3} 9.689953
{txt}Scottish Government Directorates of t..  {c |}{col 42}{res}{space 2} .9253507{col 54}{space 2} .9586247{col 65}{space 1}   -0.07{col 74}{space 3}0.940{col 82}{space 4} .1214779{col 95}{space 3} 7.048807
{txt}Scottish Government, Directorates of ..  {c |}{col 42}{res}{space 2} .7087294{col 54}{space 2} .7147007{col 65}{space 1}   -0.34{col 74}{space 3}0.733{col 82}{space 4}  .098199{col 95}{space 3} 5.115095
{txt}Scottish Executive, Environment and R..  {c |}{col 42}{res}{space 2}        1{col 54}{txt}  (omitted)
Scottish Executive, Finance & Central..  {c |}{col 42}{res}{space 2} 2.33e+07{col 54}{space 2} 2.17e+07{col 65}{space 1}   18.15{col 74}{space 3}0.000{col 82}{space 4}  3725699{col 95}{space 3} 1.45e+08
{txt}{hline 41}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * Destination: private and nonprofit sector
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 3)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 3
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}       1318{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         55{txt}  failures in single-failure-per-subject data
{res}       1345{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score  /*
> */ i.dept_this_yr if coxsample == 1, /*
> */ compete(civpubnotpub2 == 1, 4)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 3
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-179.56952}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-176.12269}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-175.82802}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-175.82672}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-175.82669}  
{res}{txt}Iteration 5:{space 3}log pseudolikelihood = {res:-175.82669}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 3{txt}{col 50}No. failed{col 67}={col 69}{res}        43
{txt}Competing events{col 17}: {res}civpubno~2 == 1 4{txt}{col 50}No. competing{col 67}={col 69}{res}        99
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{txt}Wald chi2({res}63{txt}){col 67}={col 70}{res} 23410.78
{txt}Log pseudolikelihood = {res}-175.82669{col 50}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 106:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 41}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 42}{c |}{col 54}    Robust
{col 1}                                      _t{col 42}{c |}        SHR{col 54}   Std. Err.{col 66}      z{col 74}   P>|z|{col 82}     [95% Con{col 95}f. Interval]
{hline 41}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 32}ceocivil {c |}{col 42}{res}{space 2} .3103146{col 54}{space 2} .1191757{col 65}{space 1}   -3.05{col 74}{space 3}0.002{col 82}{space 4}  .146184{col 95}{space 3} .6587256
{txt}{space 24}L_high_tarmil_FY {c |}{col 42}{res}{space 2} 1.860947{col 54}{space 2}   .75887{col 65}{space 1}    1.52{col 74}{space 3}0.128{col 82}{space 4} .8368066{col 95}{space 3}   4.1385
{txt}{space 25}L_low_tarmil_FY {c |}{col 42}{res}{space 2} .7936449{col 54}{space 2} .4664826{col 65}{space 1}   -0.39{col 74}{space 3}0.694{col 82}{space 4} .2507918{col 95}{space 3} 2.511534
{txt}{space 29}L_newparty2 {c |}{col 42}{res}{space 2} .7457959{col 54}{space 2} .4563415{col 65}{space 1}   -0.48{col 74}{space 3}0.632{col 82}{space 4} .2247928{col 95}{space 3}  2.47433
{txt}{space 31}newparty2 {c |}{col 42}{res}{space 2}  2.14319{col 54}{space 2} .9853277{col 65}{space 1}    1.66{col 74}{space 3}0.097{col 82}{space 4} .8704067{col 95}{space 3} 5.277145
{txt}{space 32}L_newMIN {c |}{col 42}{res}{space 2} 1.509366{col 54}{space 2} .5630955{col 65}{space 1}    1.10{col 74}{space 3}0.270{col 82}{space 4} .7265058{col 95}{space 3} 3.135814
{txt}{space 34}newMIN {c |}{col 42}{res}{space 2} .9974853{col 54}{space 2}  .381304{col 65}{space 1}   -0.01{col 74}{space 3}0.995{col 82}{space 4} .4715434{col 95}{space 3} 2.110043
{txt}{space 32}badegree {c |}{col 42}{res}{space 2} 1.112245{col 54}{space 2} .5842497{col 65}{space 1}    0.20{col 74}{space 3}0.840{col 82}{space 4} .3972589{col 95}{space 3}  3.11406
{txt}{space 29}ceooxbridge {c |}{col 42}{res}{space 2} 1.293679{col 54}{space 2}  .575248{col 65}{space 1}    0.58{col 74}{space 3}0.563{col 82}{space 4} .5411661{col 95}{space 3}  3.09259
{txt}{space 34}ceoage {c |}{col 42}{res}{space 2} 1.062129{col 54}{space 2} .0375126{col 65}{space 1}    1.71{col 74}{space 3}0.088{col 82}{space 4} .9910923{col 95}{space 3} 1.138256
{txt}{space 31}ceofemale {c |}{col 42}{res}{space 2} .4695979{col 54}{space 2} .3287146{col 65}{space 1}   -1.08{col 74}{space 3}0.280{col 82}{space 4} .1190946{col 95}{space 3} 1.851655
{txt}{space 35}staff {c |}{col 42}{res}{space 2} .9999976{col 54}{space 2} .0000455{col 65}{space 1}   -0.05{col 74}{space 3}0.958{col 82}{space 4} .9999085{col 95}{space 3} 1.000087
{txt}{space 30}regulatory {c |}{col 42}{res}{space 2} 1.450317{col 54}{space 2} .6984805{col 65}{space 1}    0.77{col 74}{space 3}0.440{col 82}{space 4} .5643122{col 95}{space 3} 3.727402
{txt}{space 28}trading_fund {c |}{col 42}{res}{space 2} 1.395101{col 54}{space 2} .5215796{col 65}{space 1}    0.89{col 74}{space 3}0.373{col 82}{space 4} .6704568{col 95}{space 3} 2.902954
{txt}{space 17}L_pc_effoutcome_targets {c |}{col 42}{res}{space 2} .9981399{col 54}{space 2} .0116752{col 65}{space 1}   -0.16{col 74}{space 3}0.874{col 82}{space 4} .9755173{col 95}{space 3} 1.021287
{txt}{space 24}L_targsmiles_set {c |}{col 42}{res}{space 2} 1.019214{col 54}{space 2} .0309616{col 65}{space 1}    0.63{col 74}{space 3}0.531{col 82}{space 4} .9603019{col 95}{space 3} 1.081741
{txt}{space 25}L_media_Z_score {c |}{col 42}{res}{space 2} .9605173{col 54}{space 2} .1914936{col 65}{space 1}   -0.20{col 74}{space 3}0.840{col 82}{space 4} .6498384{col 95}{space 3} 1.419728
{txt}{space 27}media_Z_score {c |}{col 42}{res}{space 2} 1.123815{col 54}{space 2} .1756335{col 65}{space 1}    0.75{col 74}{space 3}0.455{col 82}{space 4}  .827306{col 95}{space 3} 1.526593
{txt}{space 40} {c |}
{space 28}dept_this_yr {c |}
{space 25}cabinet office  {c |}{col 42}{res}{space 2} 1.171474{col 54}{space 2} 1.391744{col 65}{space 1}    0.13{col 74}{space 3}0.894{col 82}{space 4} .1141515{col 95}{space 3}  12.0222
{txt}{space 15}culture, media and sport  {c |}{col 42}{res}{space 2} 3.59e-09{col 54}{space 2} 5.07e-09{col 65}{space 1}  -13.80{col 74}{space 3}0.000{col 82}{space 4} 2.27e-10{col 95}{space 3} 5.69e-08
{txt}{space 26}defence (mod)  {c |}{col 42}{res}{space 2}  1.25157{col 54}{space 2}  1.38937{col 65}{space 1}    0.20{col 74}{space 3}0.840{col 82}{space 4} .1420804{col 95}{space 3} 11.02493
{txt}{space 8}education and employment (dfee)  {c |}{col 42}{res}{space 2} 1.08e-08{col 54}{space 2} 2.14e-08{col 65}{space 1}   -9.28{col 74}{space 3}0.000{col 82}{space 4} 2.26e-10{col 95}{space 3} 5.21e-07
{txt}{space 22}environment (doe)  {c |}{col 42}{res}{space 2} 3.71e-09{col 54}{space 2} 4.29e-09{col 65}{space 1}  -16.78{col 74}{space 3}0.000{col 82}{space 4} 3.84e-10{col 95}{space 3} 3.58e-08
{txt}environment, transport and regions (..)  {c |}{col 42}{res}{space 2} 1.029027{col 54}{space 2} 1.363635{col 65}{space 1}    0.02{col 74}{space 3}0.983{col 82}{space 4} .0766374{col 95}{space 3} 13.81697
{txt}{space 2}foreign and commonwealth office (fco)  {c |}{col 42}{res}{space 2} 9.70e-09{col 54}{space 2} 1.28e-08{col 65}{space 1}  -13.96{col 74}{space 3}0.000{col 82}{space 4} 7.27e-10{col 95}{space 3} 1.29e-07
{txt}{space 23}health (doh/ dh)  {c |}{col 42}{res}{space 2} .6244842{col 54}{space 2} .8814776{col 65}{space 1}   -0.33{col 74}{space 3}0.739{col 82}{space 4} .0392668{col 95}{space 3} 9.931552
{txt}{space 28}hm treasury  {c |}{col 42}{res}{space 2} .8576026{col 54}{space 2}  1.12632{col 65}{space 1}   -0.12{col 74}{space 3}0.907{col 82}{space 4} .0653691{col 95}{space 3} 11.25122
{txt}{space 28}home office  {c |}{col 42}{res}{space 2} 2.496629{col 54}{space 2} 3.081737{col 65}{space 1}    0.74{col 74}{space 3}0.459{col 82}{space 4}   .22216{col 95}{space 3} 28.05707
{txt}{space 11}lord chancellor's department  {c |}{col 42}{res}{space 2} 2.580061{col 54}{space 2} 3.259163{col 65}{space 1}    0.75{col 74}{space 3}0.453{col 82}{space 4} .2169613{col 95}{space 3} 30.68157
{txt}{space 18}social security (dss)  {c |}{col 42}{res}{space 2} 1.75e-08{col 54}{space 2} 3.24e-08{col 65}{space 1}   -9.65{col 74}{space 3}0.000{col 82}{space 4} 4.65e-10{col 95}{space 3} 6.60e-07
{txt}{space 15}trade and industry (dti)  {c |}{col 42}{res}{space 2} .7757835{col 54}{space 2} .9507857{col 65}{space 1}   -0.21{col 74}{space 3}0.836{col 82}{space 4} .0702302{col 95}{space 3} 8.569534
{txt}{space 27}welsh office  {c |}{col 42}{res}{space 2} 5.290422{col 54}{space 2} 6.034884{col 65}{space 1}    1.46{col 74}{space 3}0.144{col 82}{space 4} .5655989{col 95}{space 3} 49.48482
{txt}environment, food & rural affairs (d..)  {c |}{col 42}{res}{space 2} .6474895{col 54}{space 2} .8238839{col 65}{space 1}   -0.34{col 74}{space 3}0.733{col 82}{space 4} .0534737{col 95}{space 3} 7.840172
{txt}berr (business, enterprise & regulat..)  {c |}{col 42}{res}{space 2} 8.06e-09{col 54}{space 2} 1.11e-08{col 65}{space 1}  -13.56{col 74}{space 3}0.000{col 82}{space 4} 5.45e-10{col 95}{space 3} 1.19e-07
{txt}{space 2}bis (business, innovation and skills)  {c |}{col 42}{res}{space 2} 3.47e-09{col 54}{space 2} 4.04e-09{col 65}{space 1}  -16.73{col 74}{space 3}0.000{col 82}{space 4} 3.54e-10{col 95}{space 3} 3.40e-08
{txt}dius (department for innovation, uni..)  {c |}{col 42}{res}{space 2} 3.36e-09{col 54}{space 2} 4.26e-09{col 65}{space 1}  -15.37{col 74}{space 3}0.000{col 82}{space 4} 2.79e-10{col 95}{space 3} 4.04e-08
{txt}{space 2}department for constitutional affairs  {c |}{col 42}{res}{space 2} 6.25e-09{col 54}{space 2} 7.27e-09{col 65}{space 1}  -16.25{col 74}{space 3}0.000{col 82}{space 4} 6.41e-10{col 95}{space 3} 6.10e-08
{txt}{space 20}ministry of justice  {c |}{col 42}{res}{space 2} 1.599099{col 54}{space 2} 3.411743{col 65}{space 1}    0.22{col 74}{space 3}0.826{col 82}{space 4} .0244232{col 95}{space 3} 104.7005
{txt}{space 24}transport (dtp)  {c |}{col 42}{res}{space 2} 2.658154{col 54}{space 2} 2.829018{col 65}{space 1}    0.92{col 74}{space 3}0.358{col 82}{space 4}  .330114{col 95}{space 3} 21.40407
{txt}transport, local government & the re..)  {c |}{col 42}{res}{space 2} 2.601062{col 54}{space 2} 3.973571{col 65}{space 1}    0.63{col 74}{space 3}0.531{col 82}{space 4} .1302547{col 95}{space 3} 51.94072
{txt}office of the deputy prime minister ..)  {c |}{col 42}{res}{space 2} 6.65e-09{col 54}{space 2} 1.06e-08{col 65}{space 1}  -11.83{col 74}{space 3}0.000{col 82}{space 4} 2.94e-10{col 95}{space 3} 1.50e-07
{txt}department for communities & local g..)  {c |}{col 42}{res}{space 2} .6260356{col 54}{space 2} .9552765{col 65}{space 1}   -0.31{col 74}{space 3}0.759{col 82}{space 4} .0314585{col 95}{space 3} 12.45832
{txt}scottish executive, rural affairs dep..  {c |}{col 42}{res}{space 2} 12.77821{col 54}{space 2} 14.95772{col 65}{space 1}    2.18{col 74}{space 3}0.030{col 82}{space 4} 1.288497{col 95}{space 3} 126.7233
{txt}{space 23}attorney general  {c |}{col 42}{res}{space 2} 1.09e-08{col 54}{space 2} 1.40e-08{col 65}{space 1}  -14.33{col 74}{space 3}0.000{col 82}{space 4} 8.93e-10{col 95}{space 3} 1.34e-07
{txt}{space 16}work and pensions (dwp)  {c |}{col 42}{res}{space 2} 1.33e-08{col 54}{space 2} 1.67e-08{col 65}{space 1}  -14.40{col 74}{space 3}0.000{col 82}{space 4} 1.12e-09{col 95}{space 3} 1.57e-07
{txt}scottish executive, development depar..  {c |}{col 42}{res}{space 2} 1.04e-08{col 54}{space 2} 1.36e-08{col 65}{space 1}  -13.99{col 74}{space 3}0.000{col 82}{space 4} 7.91e-10{col 95}{space 3} 1.36e-07
{txt}{space 13}employment (department of)  {c |}{col 42}{res}{space 2} 6.83e-09{col 54}{space 2} 1.58e-08{col 65}{space 1}   -8.15{col 74}{space 3}0.000{col 82}{space 4} 7.42e-11{col 95}{space 3} 6.29e-07
{txt}{space 7}department for national heritage  {c |}{col 42}{res}{space 2} 1.83e-09{col 54}{space 2} 2.97e-09{col 65}{space 1}  -12.42{col 74}{space 3}0.000{col 82}{space 4} 7.65e-11{col 95}{space 3} 4.38e-08
{txt}scottish executive, education departm..  {c |}{col 42}{res}{space 2} 4.09e-09{col 54}{space 2} 5.90e-09{col 65}{space 1}  -13.41{col 74}{space 3}0.000{col 82}{space 4} 2.43e-10{col 95}{space 3} 6.89e-08
{txt}{space 24}scottish office  {c |}{col 42}{res}{space 2} 8.69e-09{col 54}{space 2} 9.46e-09{col 65}{space 1}  -17.05{col 74}{space 3}0.000{col 82}{space 4} 1.03e-09{col 95}{space 3} 7.34e-08
{txt}Scottish Government, Europe, External..  {c |}{col 42}{res}{space 2} 4.10e-09{col 54}{space 2} 6.13e-09{col 65}{space 1}  -12.89{col 74}{space 3}0.000{col 82}{space 4} 2.17e-10{col 95}{space 3} 7.71e-08
{txt}Scottish Government, Culture, Externa..  {c |}{col 42}{res}{space 2} 1.23e-08{col 54}{space 2} 1.70e-08{col 65}{space 1}  -13.15{col 74}{space 3}0.000{col 82}{space 4} 8.16e-10{col 95}{space 3} 1.86e-07
{txt}{space 1}scottish executive, justice department  {c |}{col 42}{res}{space 2} 1.47e-08{col 54}{space 2} 1.77e-08{col 65}{space 1}  -14.97{col 74}{space 3}0.000{col 82}{space 4} 1.39e-09{col 95}{space 3} 1.56e-07
{txt}{space 25}inland revenue  {c |}{col 42}{res}{space 2} 1.06e-08{col 54}{space 2} 1.35e-08{col 65}{space 1}  -14.35{col 74}{space 3}0.000{col 82}{space 4} 8.63e-10{col 95}{space 3} 1.30e-07
{txt}hmrc (her majesty's revenue and cust..)  {c |}{col 42}{res}{space 2} 6.68e-09{col 54}{space 2} 9.74e-09{col 65}{space 1}  -12.90{col 74}{space 3}0.000{col 82}{space 4} 3.83e-10{col 95}{space 3} 1.17e-07
{txt}{space 8}treasury solicitor's department  {c |}{col 42}{res}{space 2} 4.64e-09{col 54}{space 2} 7.35e-09{col 65}{space 1}  -12.13{col 74}{space 3}0.000{col 82}{space 4} 2.09e-10{col 95}{space 3} 1.03e-07
{txt}scottish government, directorates of ..  {c |}{col 42}{res}{space 2} 6.03e-09{col 54}{space 2} 1.06e-08{col 65}{space 1}  -10.75{col 74}{space 3}0.000{col 82}{space 4} 1.91e-10{col 95}{space 3} 1.90e-07
{txt}scottish executive, enterprise, trans..  {c |}{col 42}{res}{space 2} 9.71e-09{col 54}{space 2} 1.35e-08{col 65}{space 1}  -13.30{col 74}{space 3}0.000{col 82}{space 4} 6.40e-10{col 95}{space 3} 1.47e-07
{txt}scottish executive, enterprise and li..  {c |}{col 42}{res}{space 2} 1.34e-08{col 54}{space 2} 2.00e-08{col 65}{space 1}  -12.15{col 74}{space 3}0.000{col 82}{space 4} 7.23e-10{col 95}{space 3} 2.50e-07
{txt}Scottish Government Directorates of t..  {c |}{col 42}{res}{space 2}  1.44086{col 54}{space 2} 1.988372{col 65}{space 1}    0.26{col 74}{space 3}0.791{col 82}{space 4} .0963767{col 95}{space 3} 21.54127
{txt}Scottish Government, Directorates of ..  {c |}{col 42}{res}{space 2} 7.207743{col 54}{space 2}  8.62241{col 65}{space 1}    1.65{col 74}{space 3}0.099{col 82}{space 4} .6910858{col 95}{space 3} 75.17382
{txt}Scottish Executive, Environment and R..  {c |}{col 42}{res}{space 2} 6.78e-09{col 54}{space 2} 1.08e-08{col 65}{space 1}  -11.83{col 74}{space 3}0.000{col 82}{space 4} 3.01e-10{col 95}{space 3} 1.53e-07
{txt}Scottish Executive, Finance & Central..  {c |}{col 42}{res}{space 2} 1.17e-08{col 54}{space 2} 1.65e-08{col 65}{space 1}  -12.93{col 74}{space 3}0.000{col 82}{space 4} 7.36e-10{col 95}{space 3} 1.86e-07
{txt}{hline 41}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * Destination: retirement
. stset ceoduration, id(agencyCEOid) failure(civpubnotpub2 == 4)

                {txt}id:  {res}agencyCEOid
     {txt}failure event:  {res}civpubnotpub2 == 4
{txt}obs. time interval:  {res}(ceoduration[_n-1], ceoduration]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}       1318{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}       1318{txt}  observations remaining, representing
{res}        332{txt}  subjects
{res}         75{txt}  failures in single-failure-per-subject data
{res}       1345{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}       14
{txt}
{com}. stcrreg ceocivil /*
> */ L_high_tarmil_FY /*
> */ L_low_tarmil_FY /*
> */ L_newparty2 newparty2 /*
> */ L_newMIN newMIN  /*
> */ badegree /*
> */ ceooxbridge /*
> */ ceoage /*
> */ ceofemale /*
> */ staff /*
> */ regulatory /*
> */ trading_fund /*
> */ L_pc_effoutcome_targets /*
> */ L_targsmiles_set /*
> */ L_media_Z_score /*
> */ media_Z_score  /*
> */ i.dept_this_yr if coxsample == 1, /*
> */ compete(civpubnotpub2 == 1, 3)
{res}
         {txt}failure _d:  {res}civpubnotpub2 == 4
   {txt}analysis time _t:  {res}ceoduration
                 {txt}id:  {res}agencyCEOid

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-218.93788}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-213.05744}  (backed up)
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-203.43667}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-200.55823}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-200.47008}  
{res}{txt}Iteration 5:{space 3}log pseudolikelihood = {res:-200.46967}  
{res}{txt}Iteration 6:{space 3}log pseudolikelihood = {res:-200.46964}  
{res}{txt}Iteration 7:{space 3}log pseudolikelihood = {res:-200.46964}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       934
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       247
{txt}Failure event{col 17}: {res}civpubno~2 == 4{txt}{col 50}No. failed{col 67}={col 69}{res}        60
{txt}Competing events{col 17}: {res}civpubno~2 == 1 3{txt}{col 50}No. competing{col 67}={col 69}{res}        82
{txt}{col 50}No. censored{col 67}={col 69}{res}       105

{col 50}{txt}Wald chi2({res}63{txt}){col 67}={col 70}{res} 10789.89
{txt}Log pseudolikelihood = {res}-200.46964{col 50}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 106:(Std. Err. adjusted for {res:247} clusters in agencyCEOid)}
{hline 41}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 42}{c |}{col 54}    Robust
{col 1}                                      _t{col 42}{c |}        SHR{col 54}   Std. Err.{col 66}      z{col 74}   P>|z|{col 82}     [95% Con{col 95}f. Interval]
{hline 41}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 32}ceocivil {c |}{col 42}{res}{space 2} 4.153906{col 54}{space 2} 2.772797{col 65}{space 1}    2.13{col 74}{space 3}0.033{col 82}{space 4} 1.122707{col 95}{space 3} 15.36905
{txt}{space 24}L_high_tarmil_FY {c |}{col 42}{res}{space 2}  1.62473{col 54}{space 2} .7418284{col 65}{space 1}    1.06{col 74}{space 3}0.288{col 82}{space 4} .6639489{col 95}{space 3} 3.975828
{txt}{space 25}L_low_tarmil_FY {c |}{col 42}{res}{space 2} 2.102071{col 54}{space 2} .8411297{col 65}{space 1}    1.86{col 74}{space 3}0.063{col 82}{space 4} .9594994{col 95}{space 3} 4.605216
{txt}{space 29}L_newparty2 {c |}{col 42}{res}{space 2} .4247174{col 54}{space 2} .2431624{col 65}{space 1}   -1.50{col 74}{space 3}0.135{col 82}{space 4} .1382814{col 95}{space 3} 1.304477
{txt}{space 31}newparty2 {c |}{col 42}{res}{space 2}  .507249{col 54}{space 2} .3423423{col 65}{space 1}   -1.01{col 74}{space 3}0.315{col 82}{space 4}  .135128{col 95}{space 3} 1.904132
{txt}{space 32}L_newMIN {c |}{col 42}{res}{space 2} 1.060002{col 54}{space 2} .3456205{col 65}{space 1}    0.18{col 74}{space 3}0.858{col 82}{space 4} .5594573{col 95}{space 3} 2.008383
{txt}{space 34}newMIN {c |}{col 42}{res}{space 2} 1.061759{col 54}{space 2} .3782032{col 65}{space 1}    0.17{col 74}{space 3}0.866{col 82}{space 4} .5282313{col 95}{space 3} 2.134163
{txt}{space 32}badegree {c |}{col 42}{res}{space 2} .7004139{col 54}{space 2} .2680738{col 65}{space 1}   -0.93{col 74}{space 3}0.352{col 82}{space 4} .3308027{col 95}{space 3} 1.482998
{txt}{space 29}ceooxbridge {c |}{col 42}{res}{space 2} .6801954{col 54}{space 2} .3927844{col 65}{space 1}   -0.67{col 74}{space 3}0.505{col 82}{space 4} .2193312{col 95}{space 3} 2.109439
{txt}{space 34}ceoage {c |}{col 42}{res}{space 2} 1.467902{col 54}{space 2} .0689701{col 65}{space 1}    8.17{col 74}{space 3}0.000{col 82}{space 4} 1.338761{col 95}{space 3} 1.609501
{txt}{space 31}ceofemale {c |}{col 42}{res}{space 2} 1.793668{col 54}{space 2} 1.873463{col 65}{space 1}    0.56{col 74}{space 3}0.576{col 82}{space 4} .2315651{col 95}{space 3} 13.89348
{txt}{space 35}staff {c |}{col 42}{res}{space 2} 1.000032{col 54}{space 2} .0000188{col 65}{space 1}    1.73{col 74}{space 3}0.084{col 82}{space 4} .9999956{col 95}{space 3} 1.000069
{txt}{space 30}regulatory {c |}{col 42}{res}{space 2} 2.048952{col 54}{space 2} 1.312916{col 65}{space 1}    1.12{col 74}{space 3}0.263{col 82}{space 4}  .583584{col 95}{space 3}  7.19383
{txt}{space 28}trading_fund {c |}{col 42}{res}{space 2} .4574088{col 54}{space 2}  .257655{col 65}{space 1}   -1.39{col 74}{space 3}0.165{col 82}{space 4} .1516453{col 95}{space 3} 1.379686
{txt}{space 17}L_pc_effoutcome_targets {c |}{col 42}{res}{space 2} .9842705{col 54}{space 2} .0152459{col 65}{space 1}   -1.02{col 74}{space 3}0.306{col 82}{space 4} .9548382{col 95}{space 3}  1.01461
{txt}{space 24}L_targsmiles_set {c |}{col 42}{res}{space 2} .9463394{col 54}{space 2} .0322894{col 65}{space 1}   -1.62{col 74}{space 3}0.106{col 82}{space 4}  .885123{col 95}{space 3}  1.01179
{txt}{space 25}L_media_Z_score {c |}{col 42}{res}{space 2} .8865476{col 54}{space 2} .1540119{col 65}{space 1}   -0.69{col 74}{space 3}0.488{col 82}{space 4} .6307112{col 95}{space 3} 1.246159
{txt}{space 27}media_Z_score {c |}{col 42}{res}{space 2} 1.149162{col 54}{space 2} .1861902{col 65}{space 1}    0.86{col 74}{space 3}0.391{col 82}{space 4} .8365031{col 95}{space 3} 1.578684
{txt}{space 40} {c |}
{space 28}dept_this_yr {c |}
{space 25}cabinet office  {c |}{col 42}{res}{space 2} .7900561{col 54}{space 2} .8858473{col 65}{space 1}   -0.21{col 74}{space 3}0.834{col 82}{space 4} .0877509{col 95}{space 3} 7.113189
{txt}{space 15}culture, media and sport  {c |}{col 42}{res}{space 2} .4509041{col 54}{space 2} .4636562{col 65}{space 1}   -0.77{col 74}{space 3}0.439{col 82}{space 4}  .060091{col 95}{space 3} 3.383442
{txt}{space 26}defence (mod)  {c |}{col 42}{res}{space 2} .9067749{col 54}{space 2}  .764326{col 65}{space 1}   -0.12{col 74}{space 3}0.908{col 82}{space 4} .1737867{col 95}{space 3}  4.73132
{txt}{space 8}education and employment (dfee)  {c |}{col 42}{res}{space 2} 6.35e-10{col 54}{space 2} 8.51e-10{col 65}{space 1}  -15.79{col 74}{space 3}0.000{col 82}{space 4} 4.58e-11{col 95}{space 3} 8.79e-09
{txt}{space 22}environment (doe)  {c |}{col 42}{res}{space 2} 4.850846{col 54}{space 2} 4.841923{col 65}{space 1}    1.58{col 74}{space 3}0.114{col 82}{space 4} .6857749{col 95}{space 3} 34.31258
{txt}environment, transport and regions (..)  {c |}{col 42}{res}{space 2} .3860088{col 54}{space 2} .3071222{col 65}{space 1}   -1.20{col 74}{space 3}0.232{col 82}{space 4} .0811617{col 95}{space 3} 1.835875
{txt}{space 2}foreign and commonwealth office (fco)  {c |}{col 42}{res}{space 2} 3.929463{col 54}{space 2} 4.104823{col 65}{space 1}    1.31{col 74}{space 3}0.190{col 82}{space 4} .5071604{col 95}{space 3} 30.44537
{txt}{space 23}health (doh/ dh)  {c |}{col 42}{res}{space 2} 4.13e-09{col 54}{space 2} 4.22e-09{col 65}{space 1}  -18.90{col 74}{space 3}0.000{col 82}{space 4} 5.58e-10{col 95}{space 3} 3.06e-08
{txt}{space 28}hm treasury  {c |}{col 42}{res}{space 2} 5.690686{col 54}{space 2} 5.841845{col 65}{space 1}    1.69{col 74}{space 3}0.090{col 82}{space 4} .7609445{col 95}{space 3} 42.55752
{txt}{space 28}home office  {c |}{col 42}{res}{space 2} 1.683426{col 54}{space 2}  1.89197{col 65}{space 1}    0.46{col 74}{space 3}0.643{col 82}{space 4} .1860136{col 95}{space 3} 15.23502
{txt}{space 11}lord chancellor's department  {c |}{col 42}{res}{space 2} 6.28e-09{col 54}{space 2} 7.82e-09{col 65}{space 1}  -15.16{col 74}{space 3}0.000{col 82}{space 4} 5.46e-10{col 95}{space 3} 7.22e-08
{txt}{space 18}social security (dss)  {c |}{col 42}{res}{space 2} 1.469497{col 54}{space 2} 2.022072{col 65}{space 1}    0.28{col 74}{space 3}0.780{col 82}{space 4} .0990582{col 95}{space 3} 21.79953
{txt}{space 15}trade and industry (dti)  {c |}{col 42}{res}{space 2} .6154265{col 54}{space 2} .5429219{col 65}{space 1}   -0.55{col 74}{space 3}0.582{col 82}{space 4} .1092085{col 95}{space 3} 3.468136
{txt}{space 27}welsh office  {c |}{col 42}{res}{space 2} 5.76e-08{col 54}{space 2} 9.04e-08{col 65}{space 1}  -10.62{col 74}{space 3}0.000{col 82}{space 4} 2.65e-09{col 95}{space 3} 1.25e-06
{txt}environment, food & rural affairs (d..)  {c |}{col 42}{res}{space 2} .5036184{col 54}{space 2} .4944055{col 65}{space 1}   -0.70{col 74}{space 3}0.485{col 82}{space 4} .0735312{col 95}{space 3} 3.449306
{txt}berr (business, enterprise & regulat..)  {c |}{col 42}{res}{space 2} .9540412{col 54}{space 2} .8547045{col 65}{space 1}   -0.05{col 74}{space 3}0.958{col 82}{space 4} .1648141{col 95}{space 3} 5.522552
{txt}{space 2}bis (business, innovation and skills)  {c |}{col 42}{res}{space 2} 1.14e-08{col 54}{space 2} 1.42e-08{col 65}{space 1}  -14.74{col 74}{space 3}0.000{col 82}{space 4} 1.01e-09{col 95}{space 3} 1.30e-07
{txt}dius (department for innovation, uni..)  {c |}{col 42}{res}{space 2} 1.32e-09{col 54}{space 2} 1.77e-09{col 65}{space 1}  -15.28{col 74}{space 3}0.000{col 82}{space 4} 9.60e-11{col 95}{space 3} 1.82e-08
{txt}{space 2}department for constitutional affairs  {c |}{col 42}{res}{space 2} 3.460111{col 54}{space 2} 4.025678{col 65}{space 1}    1.07{col 74}{space 3}0.286{col 82}{space 4} .3537991{col 95}{space 3} 33.83945
{txt}{space 20}ministry of justice  {c |}{col 42}{res}{space 2} .8433806{col 54}{space 2} .8800848{col 65}{space 1}   -0.16{col 74}{space 3}0.870{col 82}{space 4} .1090881{col 95}{space 3} 6.520332
{txt}{space 24}transport (dtp)  {c |}{col 42}{res}{space 2} .1850444{col 54}{space 2} .2641209{col 65}{space 1}   -1.18{col 74}{space 3}0.237{col 82}{space 4} .0112804{col 95}{space 3} 3.035484
{txt}transport, local government & the re..)  {c |}{col 42}{res}{space 2} 3.91e-09{col 54}{space 2} 3.91e-09{col 65}{space 1}  -19.39{col 74}{space 3}0.000{col 82}{space 4} 5.53e-10{col 95}{space 3} 2.77e-08
{txt}office of the deputy prime minister ..)  {c |}{col 42}{res}{space 2} 3.01e-07{col 54}{space 2} 5.32e-07{col 65}{space 1}   -8.49{col 74}{space 3}0.000{col 82}{space 4} 9.37e-09{col 95}{space 3} 9.65e-06
{txt}department for communities & local g..)  {c |}{col 42}{res}{space 2} 2.28e-08{col 54}{space 2} 3.04e-08{col 65}{space 1}  -13.21{col 74}{space 3}0.000{col 82}{space 4} 1.68e-09{col 95}{space 3} 3.11e-07
{txt}scottish executive, rural affairs dep..  {c |}{col 42}{res}{space 2} 2.66e-09{col 54}{space 2} 3.42e-09{col 65}{space 1}  -15.32{col 74}{space 3}0.000{col 82}{space 4} 2.12e-10{col 95}{space 3} 3.32e-08
{txt}{space 23}attorney general  {c |}{col 42}{res}{space 2} 1.323872{col 54}{space 2} 1.868715{col 65}{space 1}    0.20{col 74}{space 3}0.842{col 82}{space 4} .0832395{col 95}{space 3} 21.05533
{txt}{space 16}work and pensions (dwp)  {c |}{col 42}{res}{space 2} .6069086{col 54}{space 2} .5286023{col 65}{space 1}   -0.57{col 74}{space 3}0.566{col 82}{space 4}   .11009{col 95}{space 3} 3.345791
{txt}scottish executive, development depar..  {c |}{col 42}{res}{space 2} 3.15e-08{col 54}{space 2} 4.21e-08{col 65}{space 1}  -12.89{col 74}{space 3}0.000{col 82}{space 4} 2.28e-09{col 95}{space 3} 4.35e-07
{txt}{space 13}employment (department of)  {c |}{col 42}{res}{space 2} 9.85e-10{col 54}{space 2} 1.42e-09{col 65}{space 1}  -14.42{col 74}{space 3}0.000{col 82}{space 4} 5.88e-11{col 95}{space 3} 1.65e-08
{txt}{space 7}department for national heritage  {c |}{col 42}{res}{space 2} 1.50e-09{col 54}{space 2} 2.34e-09{col 65}{space 1}  -13.02{col 74}{space 3}0.000{col 82}{space 4} 7.05e-11{col 95}{space 3} 3.20e-08
{txt}scottish executive, education departm..  {c |}{col 42}{res}{space 2} 1.844264{col 54}{space 2} 1.437444{col 65}{space 1}    0.79{col 74}{space 3}0.432{col 82}{space 4} .4002999{col 95}{space 3} 8.496905
{txt}{space 24}scottish office  {c |}{col 42}{res}{space 2} 1.570329{col 54}{space 2} 1.242394{col 65}{space 1}    0.57{col 74}{space 3}0.568{col 82}{space 4} .3330787{col 95}{space 3} 7.403451
{txt}Scottish Government, Europe, External..  {c |}{col 42}{res}{space 2} 4.54e-09{col 54}{space 2} 6.61e-09{col 65}{space 1}  -13.20{col 74}{space 3}0.000{col 82}{space 4} 2.62e-10{col 95}{space 3} 7.87e-08
{txt}Scottish Government, Culture, Externa..  {c |}{col 42}{res}{space 2} 3.619174{col 54}{space 2} 3.628854{col 65}{space 1}    1.28{col 74}{space 3}0.200{col 82}{space 4} .5071439{col 95}{space 3} 25.82782
{txt}{space 1}scottish executive, justice department  {c |}{col 42}{res}{space 2} 1.871292{col 54}{space 2} 1.855825{col 65}{space 1}    0.63{col 74}{space 3}0.527{col 82}{space 4} .2679017{col 95}{space 3} 13.07096
{txt}{space 25}inland revenue  {c |}{col 42}{res}{space 2} 3.737784{col 54}{space 2} 3.215569{col 65}{space 1}    1.53{col 74}{space 3}0.125{col 82}{space 4} .6923663{col 95}{space 3} 20.17866
{txt}hmrc (her majesty's revenue and cust..)  {c |}{col 42}{res}{space 2} 2.51e-07{col 54}{space 2} 3.83e-07{col 65}{space 1}   -9.98{col 74}{space 3}0.000{col 82}{space 4} 1.27e-08{col 95}{space 3} 4.97e-06
{txt}{space 8}treasury solicitor's department  {c |}{col 42}{res}{space 2} 3.000892{col 54}{space 2} 2.763331{col 65}{space 1}    1.19{col 74}{space 3}0.233{col 82}{space 4} .4936656{col 95}{space 3}  18.2418
{txt}scottish government, directorates of ..  {c |}{col 42}{res}{space 2} .3826069{col 54}{space 2} .3698954{col 65}{space 1}   -0.99{col 74}{space 3}0.320{col 82}{space 4} .0575216{col 95}{space 3} 2.544923
{txt}scottish executive, enterprise, trans..  {c |}{col 42}{res}{space 2} 1.98e-09{col 54}{space 2} 2.40e-09{col 65}{space 1}  -16.48{col 74}{space 3}0.000{col 82}{space 4} 1.82e-10{col 95}{space 3} 2.14e-08
{txt}scottish executive, enterprise and li..  {c |}{col 42}{res}{space 2} 5.32e-09{col 54}{space 2} 6.79e-09{col 65}{space 1}  -14.93{col 74}{space 3}0.000{col 82}{space 4} 4.37e-10{col 95}{space 3} 6.49e-08
{txt}Scottish Government Directorates of t..  {c |}{col 42}{res}{space 2} 1.272933{col 54}{space 2}  2.03955{col 65}{space 1}    0.15{col 74}{space 3}0.880{col 82}{space 4} .0550773{col 95}{space 3} 29.41974
{txt}Scottish Government, Directorates of ..  {c |}{col 42}{res}{space 2} 2.184801{col 54}{space 2} 3.387127{col 65}{space 1}    0.50{col 74}{space 3}0.614{col 82}{space 4} .1046603{col 95}{space 3} 45.60805
{txt}Scottish Executive, Environment and R..  {c |}{col 42}{res}{space 2} 3.529992{col 54}{space 2} 3.185635{col 65}{space 1}    1.40{col 74}{space 3}0.162{col 82}{space 4} .6020165{col 95}{space 3} 20.69851
{txt}Scottish Executive, Finance & Central..  {c |}{col 42}{res}{space 2} 9.32e-09{col 54}{space 2} 1.35e-08{col 65}{space 1}  -12.80{col 74}{space 3}0.000{col 82}{space 4} 5.50e-10{col 95}{space 3} 1.58e-07
{txt}{hline 41}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. clear
{txt}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/np/Downloads/PetrovskyJamesMoseleyBoynePAR.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 9 Mar 2017, 17:10:30
{txt}{.-}
{smcl}
{txt}{sf}{ul off}